Your Role

Ascend is hiring a Solutions Engineer (Contract) to take full ownership of critical AI agentic workflows. You’ll shape how automation powers the insurance industry—improving accuracy, scalability, and customer experience at every step.

You’ll dive deep into complex data problems, fine-tune prompts, and refine systems that power the automation layer behind Ascend’s financial infrastructure. This role is a mix of hands-on problem-solving, experimentation, and collaboration with Product, Operations, and Engineering teams to make our automation more reliable and intelligent.

This is a contract role based on-site at our San Francisco office, 40 hours per week for six months.

Responsibilities

Own and optimize prompt logic

• Build, test, and refine prompts that power AI agents using prompt engineering and analytical problem-solving.
• Design systems that enable scalable, high-accuracy iteration on prompt workflows.

Technical implementation & tooling

• Partner with internal teams to improve testing, monitoring, and refinement processes.
• Support new customer and organization onboarding through internal tooling improvements.
• Provide feedback and insights to shape the broader product roadmap.

Customer & stakeholder collaboration

• Work closely with internal and external users to gather context, troubleshoot challenges, and deliver consistent, high-quality results.

Quality assurance & reporting

• Track and analyze AI workflow accuracy metrics, uncovering trends and root causes.
• Recommend process improvements to reduce manual work over time.
• Build reusable playbooks and prompt strategies to support future automation efforts.

Why This Role Matters

This is a chance to own a critical piece of Ascend’s AI-powered automation systems and see your work directly impact product performance, customer satisfaction, and company growth.

You’ll thrive here if you love solving tough technical problems, moving quickly, and building systems that scale intelligently. This role is perfect for someone who operates at the intersection of data, engineering, and product thinking—and takes pride in turning complexity into clarity.

What We’re Looking For

• Exceptional analytical and critical thinking skills, with a track record of breaking down complex problems into actionable solutions.
• Experience with prompt engineering, AI agents, or AI-powered automation workflows.
• Proven ability to work with complex, real-world systems to identify patterns and improve reliability.
• Strong communication skills, with the ability to translate technical concepts for both technical and non-technical audiences.
• Bonus: Familiarity with large language models (LLMs) and their applications in automation or data systems.

Contract Details

• Location: On-site in San Francisco (SoMa)
• Schedule: Full-time, 40 hours per week
• Type: Contract to hire as a full time software engineer (6 months)

About the Role

You’ll join the Data Platform team, responsible for building the backend services and “data products” that power how data moves through the company. We create the core platform primitives — high-quality event streams, reliable access layers, and developer-friendly APIs/tools — so teams across the org can self-serve what they need and ship faster. You’ll contribute to backend services that create value from our company data, and help make our data platform more self-serve so product and engineering teams can easily create and operate event-driven architectures, publish/consume streams, define access models, and ship data products end-to-end. You’ll also work on LLM-adjacent services such as prompt categorization/taxonomy, enrichment, and metadata systems that turn raw telemetry into trusted, usable products — with mentorship and support from experienced engineers.

Responsibilities

• Contribute to backend services that enhance the data platform’s capabilities (APIs, control planes, automation, governance).
• Help enable DIY workflows for teams across the company:

• • Define/publish events and schemas
  • Create/consume streams and subscriptions
  • Establish access models (authz, row/field-level controls where applicable)
  • Manage dataset/catalog metadata, lineage, versioning, and retention

• Contribute to end-to-end data products: ingestion → validation/quality → enrichment → serving (APIs/streams) → observability → adoption.
• Work on prompt categorization and enrichment services: taxonomy design, labeling workflows, classifier/rules integration, evaluation, drift/quality monitoring, and safe rollouts.
• Learn to own reliability: SLOs, alerting, performance/cost tuning, incident response, and postmortems.
• Partner cross-functionally with ML/LLM, infra, security, and product teams to define crisp contracts and deliver durable platform primitives.

Requirements

• 0–4 years building production or project-based backend systems (internships, coursework, and personal projects count).
• Solid fundamentals in at least one backend language (e.g., Go, Python, Java, Rust) and some exposure to API design (REST).
• Eagerness to own work end-to-end: design docs, implementation, testing, deployment, and iteration based on real usage.
• Strong engineering fundamentals: clean, maintainable code, thoughtful abstractions, and a desire to build systems that are easy to evolve.
• Basic data modeling and SQL skills, and some familiarity with at least one of:

• • Streaming/eventing (Kafka/PubSub/Kinesis, etc.)
  • Workflow/compute (Airflow/Spark/Flink/Trino, etc.)
  • OLTP/OLAP stores and data lakes (Postgres + warehouse/lake tech)

• AI augmentation curiosity:

• • You’re curious about how engineers use AI/LLMs to build software faster and better (e.g., coding copilots, agentic workflows, retrieval/knowledge grounding), and you’re eager to apply this to your own work.
  • You understand that AI tools can fail or create issues, and you’re thoughtful about when and how to apply them.

Nice to have

• Any exposure to self-serve platforms, developer tooling, or multi-tenant services.
• Coursework or projects involving LLM/AI products: prompt/response telemetry, eval datasets, embeddings/RAG metadata, model/tool traces, privacy-safe logging.
• Passion for good quality code, highly readable, SOLID principles, design patterns, Domain Driven Design.
• Awareness of security and governance basics: least-privilege access, auditability, data retention, PII handling.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $120,000 - $170,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Please see our privacy policy at https://www.together.ai/privacy  

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Stripe Assistant team is transforming how users interact with Stripe by building an intelligent and proactive assistant that not only answers users’ queries but efficiently resolves issues and provides valuable business insights. We leverage LLMs and agentic systems to elevate the user experience across Stripe—from the dashboard to support surfaces—and we enable other teams to build and integrate their AI agents on our platform. We’re evolving from a helpful support tool to a trusted pilot that anticipates, optimizes, and executes on behalf of our users.

What you’ll do

As a Senior Machine Learning Engineer on the Stripe Assistant team, you’ll own the end-to-end ML and agent architecture that makes Stripe Assistant safe, reliable, and deeply useful. You’ll set the strategy for how the Assistant executes high-trust actions, delivers accurate analytical answers across Stripe and the broader web, orchestrates capabilities across many tools and agents, and grounds responses in authoritative Stripe and user data—so users can resolve issues quickly and confidently.

You’ll drive conversation continuity and personalization across surfaces, evolve the Assistant into a proactive partner that anticipates user needs, and deepen its presence in the dashboard to streamline critical workflows. You’ll establish rigorous evaluation and SLOs and deliver step‑change improvements in quality, latency, cost, and availability—paving the way for configurable levels of autonomy and, ultimately, a dependable operating layer over a merchant’s Stripe account.

Responsibilities

Our team operates fluidly and here are some problems you may tackle:

• Establish trustworthy, human-in-the-loop execution for high-trust “write” actions—prioritizing user control, transparency, accountability, and auditability so customers can delegate with confidence.
• Define and evolve the Assistant’s capability and governance model across hundreds of tools and agents, balancing power, permissions, and consistency at scale.
• Raise answer quality and usefulness by grounding in authoritative Stripe knowledge and live user data, building cross-surface memory and personalization, and making the Assistant proactive and present in the dashboard.
• Explore and apply optimal machine learning methods to improve Stripe Assistant’s overall performance, including but not limited to fine-tuning LLMs with RLHF, synthetic data generation, optimizing RAG pipelines via domain‑specific embedding and retriever fine‑tuning, and automatic prompt tuning, etc.
• Make quality and reliability a product: set and meet SLOs, build rigorous evaluation and benchmarking loops, and drive sustained improvements in latency, cost, and availability.
• Lead as a tech lead: mentor and grow engineers, uphold high bars for code quality, security, observability, and operational rigor, and align cross‑functionally to ship safely and fast.

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 5+ years in AI/ML and backend engineering.
• Applied LLM experience: RAG/embeddings, tool use/function calling, agentic planning/orchestration, fine-tuning, code generation, evaluations, etc.
• Proficient in Python (Ruby is a plus); strong distributed systems fundamentals.
• Experience working closely with product management, design, other engineers, and other cross-functional partners.

Preferred qualifications

• Experience operating ML systems at global scale with stringent SLOs—balancing reliability, latency, and cost—with privacy, security, and compliance by design.
• Experience building products where AI/ML is core; as well as balancing short-term product priorities with long-term AI/ML improvements.
• Track record building ML platforms, especially those that enable multiple teams to collaborate together.
• Strong technical leadership and communication: mentoring and elevating engineers, elevating AI/ML awareness and posture within organizations, setting architectural direction, and driving alignment in ambiguity.

Join us to build a trustworthy, proactive AI operating layer for every Stripe merchant—advancing safety, reliability, and insight at global scale. If you’re ready to help take Stripe Assistant from copilot to full autopilot and shape how businesses connect with Stripe, we’d love to hear from you.

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

AI is moving fast and Stripe is moving fast to adopt it. But we need to make sure we do all of this safely, and for most of these tools enterprise-grade security isn't just available off the shelf. That's where the AI security team comes in - we are responsible for building and partnering to create the right security primitives to enable Stripe to move fast safely.

This is a team that will be working in the low level components of our AI stack so both infrastructure and security experience are pluses. This space is moving fast so being comfortable with ambiguity and changes in priority are a must.

This is a foundational role — you'd be the first engineer on the team.

What you’ll do

Engineers on this team will do a mixture of partnering and advising with teams building AI infrastructure and products, and building security infrastructure primitives for AI. 

One of our major focuses will be building a security tooling layer that sits in the inference path, providing detection and protection against AI specific threats like prompt injection, jailbreaks, and tool misuse.

Responsibilities

• Build security frameworks integrated with our core AI infrastructure
• Design and implement defenses against AI specific threats like prompt injection and jailbreaks
• Collaborate with other engineering teams to ensure Stripe's AI infrastructure and products are built securely
• Support other engineering teams in integrating security primitives
• Uphold our high engineering standards and improve our codebase and processes
• Design and build systems that help define the state of the art for AI security

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 5+ years of software development experience
• Experience independently designing and developing medium to large projects
• Experience working collaboratively across multiple engineering teams
• Experience writing high quality code in a major programming language
• Ability to drive clear next steps when working in an ambiguous environment
• Ability to think creatively and reason clearly in a complex problem domain

Preferred qualifications

• Experience developing, deploying, and maintaining reliable infrastructure systems
• Experience in security engineering
• Experience building AI agents or LLM-based systems

About the Role

The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.

Within Cloud Inference, the model & inference launch team owns the validation pipeline for our inference server and load balancer on these platforms. We're responsible for every inference change — model launches, performance improvements, safeguard integrations — landing on cloud platforms with correctness, performance, and reliability intact.

This is high-leverage infrastructure work: validation has to be fast and cheap enough to run on the same accelerators that serve customers, trustworthy enough to replace manual checks, and consistent enough that a change working on Anthropic first-party means it works everywhere. This directly determines how fast frontier models and features ship to every cloud platform, and how quickly performance wins reach production — reclaiming capacity at a time when compute is our scarcest resource.

What You'll Do

• Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first-party platform
• Work with the core inference team to bring new inference features (e.g. structured sampling, prompt caching, and more) to cloud platforms, owning the platform-specific integration that gets them to production
• Identify and dive deep on the gaps that make inference behave differently across first-party and CSPs — config drift, observability, deployment patterns, hard cross-platform bugs — and fix them at the source rather than building platform-specific workarounds
• Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines (throughput and latency), and correctness checks that catch regressions before production
• Drive down merge-to-production cycle time by making validation faster, more parallel, and cost-effective enough to run on the same constrained accelerator pool that serves customers, without trading away reliability 
• Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads

You May Be a Good Fit If You:

• Have a strong interest in LLM serving; prior inference or ML experience is not required 
• Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
• Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability
• Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration
• Thrive in cross-functional collaboration with both internal teams and external partners
• Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
• Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description

Strong Candidates May Also Have Experience With:

• LLM inference optimization, batching, and caching strategies
• Capacity-constrained scheduling or shared-resource test infrastructure
• Solid understanding of multi-region deployments, request routing, load balancing, global traffic management
• Working with CSP partner teams to scale infrastructure across multiple platforms, navigating differences in networking, security, privacy, and managed service
• Proficiency in Python or Rust

Programmable DNA, the software of Life, is finally here! Twist Bioscience is developing a disruptive Synthetic DNA technology that will change the world, enabling widespread health and sustainability. Synthetic biologists will use our products to engineer how organisms produce cures to diseases, make everyday chemicals by using the atmosphere as the carbon source, enable plants to make their own fertilizers, and to create in-vivo diagnostics that alert us when we are sick, and many more applications.

Reporting to the Director of Commercial Project Management, the Senior Project Manager, Custom Antibodies will coordinate multiple simultaneous projects for our high-throughput Antibody/IgG offerings. You will manage project execution from the initial inquiry through final delivery. This includes managing communication between several internal departments—Sales, Customer Service, Tech Support, Operations, Product Management, Finance etc—to create quotes/Statements of Work (SOWs) and ensure customers receive proactive updates on their project’s progress.

What You’ll Be Doing

• Manage End-to-End Coordination: Oversee custom Antibody projects from intake to final delivery, ensuring smooth transitions between quoting, production, and Antibody/data delivery.
• Design & Quoting: Gather antibody design requirements to ensure quotes and SOWs accurately reflect customer needs, accurate costing, and the production team's capabilities.
• Track & Report: Prepare quotes and maintain real-time status updates in the project tracker for all issued quotations and active orders.
• Customer Communication: Serve as the primary contact for project timelines, data quality, and technical updates; respond to inquiries or route them to the correct internal expert for prompt follow-up.
• System Accuracy: Maintain precise records of quotes, orders in progress, and shipments to ensure reliable reporting.
• Process Improvement: Identify workflow inefficiencies and suggest practical ways to scale operations as the department grows.

What You’ll Bring to the Team

• Education: Bachelor’s degree in Biology, Chemistry, or a related field. An advanced degree or hands-on laboratory experience is a plus.
• Experience: 4–5 years in the life sciences. A technical background in antibody engineering and/or antibody characterization is preferred. Familiarity with Salesforce and Jira is a plus.
• Project Management: 1–2 years of experience leading scientific projects in a commercial setting, with a proven ability to manage timelines across different departments.
• Collaborative Influence: Ability to work effectively with customers and internal teams (Sales, Operations, Customer & Tech Support, Finance, etc) to move projects forward without direct authority over those teams.
• Operational Skills: Strong organizational and communication skills, with the ability to meet deadlines and maintain high standards while under pressure.
• Problem-Solving & Scalability: A track record of identifying bottlenecks in a workflow and implementing practical, organized solutions that help a team handle more volume.

About Twist Bioscience

Twist Bioscience synthesizes genes from scratch, known as “writing” DNA. Just as children learn to both read and write, the next phase of development for the genomics revolution is the ability to write DNA.

At Twist Bioscience, we work in service of people who are changing the world for the better. In fields such as health care, agriculture, industrial chemicals and data storage, our unique silicon-based DNA Synthesis Platform provides precision at a scale that is otherwise unavailable to our customers.

Twist Bioscience Corporation is an Equal Opportunity Employer. Twist Bioscience Corporation provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic characteristics, or any other category protected by law.

Join Airtable as a Product Security Engineer and play a pivotal role in shaping the security of our rapidly evolving platform as we expand our AI and LLM-powered offerings. You will join the team responsible for safeguarding the application layer of Airtable’s platform.

You will partner closely with product engineering teams to build paved roads, frameworks, and automated controls that make the secure path the easy path for our engineering teams. You will help influence application security at scale, ensuring our products are secure by design.

What you'll do

• Develop self-service security frameworks and "paved roads" that allow engineering teams to ship secure code by default.
• Focus on automated guardrails for common vulnerabilities, while prioritizing deep-dive design reviews into complex business logic and data isolation issues (for example, multi-tenant isolation and authorization/permission bypasses) that automated tools cannot catch.
• Partner with product and engineering teams to review designs early, contribute to threat modeling for new features and complex initiatives, and provide clear, actionable security guidance.
• Research emerging threats and evolving best practices, specifically regarding AI and LLM safety, and implement controls to secure these workflows.
• Manage and evolve our approach to external penetration testing and bug bounties, driving remediation for findings and treating vulnerability management as an engineering problem.
• Contribute to the long-term roadmaps, metrics, and strategic planning for the security team.
• (Senior/Staff L5+) Lead complex threat modeling sessions for major product launches and define secure coding standards, and actively mentor other engineers to raise the technical security bar across the organization.

Who you are

• 4+ years of experience in product security or application security, with experience shipping production code. Please note this is not an early career position.
• You have a strong background in computer science or a related field, with proficiency in writing clean, maintainable code.
• You have deep familiarity with JavaScript or TypeScript, Node.js, and modern web application frameworks, and can reason about the security implications of systems built on them.
• You have hands-on experience securing LLM integrations and identifying prompt injection or data leakage risks.
• You are proficient in writing and reviewing code and treat security as an engineering problem to be solved with software, not just policies.
• You excel at communicating complex security risks to non-security stakeholders and enjoy collaborating cross-functionally to find solutions that balance security with engineering velocity.
• You are comfortable working in a fast-paced environment, navigating ambiguity, continuously learning about emerging threats and technologies, and contributing to long-term security strategy.

Airtable is an equal opportunity employer. We embrace diversity and strive to create a workplace where everyone has an equal opportunity to thrive. We welcome people of different backgrounds, experiences, abilities, and perspectives. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status or any characteristic protected by applicable federal and state laws, regulations and ordinances. Learn more about your EEO rights as an applicant. 

VEVRAA-Federal Contractor

If you have a medical condition, disability, or religious belief/practice which inhibits your ability to participate in any part of the application or interview process, please complete our Accommodations Request Form and let us know how we may assist you. Airtable is committed to participating in the interactive process and providing reasonable accommodations to qualified applicants.

#LI-remote

About the role

Anthropic is building Treasury with scale and automation in mind from the start. We're creating modern, well-controlled processes that can grow with the business; supported by strong systems, clear governance, and practical use of AI-enabled tools. We're designing touchless, AI-native workflows from day one, with SOX-grade controls built in rather than bolted on.

This role reports to the Treasury Ops Lead and plays a central part in building and running the company's Treasury Operations foundation. You'll manage core activities: daily cash positioning, global bank account administration, intercompany funding, payment controls, and Treasury reporting - while improving processes so the function scales efficiently as the company grows.

This is a hands-on role for someone who enjoys both execution and process building. You should be comfortable running key day-to-day activities while identifying opportunities to improve, automate, and strengthen controls.

You'll be the second person in a Treasury Ops function at a company that's growing fast and building deliberately. The processes you design in your first six months are the ones the company runs on at 10x scale. The ownership is end-to-end, and the toolkit is ours - you'll design Treasury workflows that are AI-native from the first draft, the way only Anthropic can.

What you'll do:

Core Treasury Operations

• Manage daily cash positioning across entities and currencies, including tracking balances and consolidating cash activity

• Lead global bank account management; account opening and closing coordination, signer updates, KYC documentation, and portal administration

• Help design and manage intercompany settlement and funding processes, including documentation and ongoing improvement

• Maintain short-term cash forecasting, including variance tracking and reporting

• Support payment workflows and controls; approval routing, exception handling, operational process improvements

• Support Letter of Credit and Bank Guarantee issuance and post-M&A Treasury integration activities

Systems & Automation

• Support TMS implementation, bank connectivity, reporting enhancements, and process standardization

• Design the "Claudification" layer: identify which workflows go touchless, build the automation, keep humans in the decisions that need judgment

• Partner with Finance Systems to simplify and automate Treasury processes

• Own Treasury data quality - the source of truth that everything downstream depends on

Controls & Governance

• Execute Treasury processes with strong focus on controls, documentation, segregation of duties, and audit readiness

• Support development and maintenance of Treasury policies, procedures, and control documentation

• Document Treasury controls for SOX readiness; not as a separate project, but as how the work gets done

• Partner with Accounting on close activities, reporting, and audit requests

What we're looking for:

• 5–8 years in Treasury operations, ideally at a growth-stage or scaling company where you saw processes get built, not just inherited

• You currently own the daily cash position. You log into the bank portals, you run the position, you execute the wires. This is a doing role, not a managing role

• Treasury-native depth. You came up through Treasury; TMS, KYC mechanics, bank account administration, cash positioning judgment. This isn't a payments-processing seat; it's a Treasury seat

• Player-coach DNA. You still do the work; run the cash position, chase the KYC doc, troubleshoot the wire - while building the system so nobody has to do it manually next quarter

• Hands-on TMS experience. You've worked in a Treasury Management System (Kyriba, GTreasury, Trovata, Quantum, or similar): configured it, broken it, fixed it. Implementation experience is a plus but not required

• SOX in your DNA. You think in audit trails, approval chains, and segregation of duties; not because compliance makes you, but because well-controlled processes are the ones that scale

• Comfort with ambiguity. There's no playbook here. You'll write it

• AI-first mindset. Not a prompt engineer; a Treasury person who wants the model handling data so you can focus on decisions.

Strong candidates may also have:

• Exposure to SOX readiness or IPO prep, even as a contributor

• Multi-entity, multi-currency operations background

• Worked with bank APIs or Treasury aggregators (Trovata, Kyriba, or direct connectivity)

• APAC or EMEA bank account opening experience

As a Software Engineer on the ML Infrastructure team, you will design and build platforms for scalable, reliable, and efficient serving of LLMs. Our platform powers cutting-edge research and production systems, supporting both internal and external use cases across various environments.

The ideal candidate combines strong ML fundamentals with deep expertise in backend system design. You’ll work in a highly collaborative environment, bridging research and engineering to deliver seamless experiences to our customers and accelerate innovation across the company.

You will:

• Build and maintain fault-tolerant, high-performance systems for serving LLMs workloads at scale.
• Build an internal platform to empower LLM capability discovery.
• Collaborate with researchers and engineers to integrate and optimize models for production and research use cases.
• Conduct architecture and design reviews to uphold best practices in system design and scalability.
• Develop monitoring and observability solutions to ensure system health and performance.
• Lead projects end-to-end, from requirements gathering to implementation, in a cross-functional environment. 

Ideally you'd have:

• 5+ years of experience building large-scale, high-performance backend systems.
• Strong programming skills in one or more languages (e.g., Python, Go, Rust, C++).
• Experience with LLM serving and routing fundamentals (e.g. rate limiting, token streaming, load balancing, budgets, etc.)
• Experience with LLM capabilities and concepts such as reasoning, tool calling, prompt templates, etc.
• Experience with containers and orchestration tools (e.g., Docker, Kubernetes).
• Familiarity with cloud infrastructure (AWS, GCP) and infrastructure as code (e.g., Terraform).
• Proven ability to solve complex problems and work independently in fast-moving environments.

Nice to haves:

• Experience with modern LLM serving frameworks such as vLLM, SGLang, TensorRT-LLM, or text-generation-inference.

The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes:

• Create multi-layered guardrails around agents
• Optimize data retrieval for agents
• Orchestrate fleets of asynchronous agents
• Automatically alerts users to deviations in data
• Illustrating how an agent reached a decision

As a Staff Software Engineer, you will orchestrate the implementation of vertical features and horizontal capabilities to include mentoring other engineers on defining requirements with stakeholders and communication tradeoffs of technical implementations on feature and capabilities until they are accepted by the stakeholders.

You will: Orchestrate feature implementation across the Federal engineering team to ensure architectural consistency.

• Define technical strategy for agentic guardrails, explainability, and fleet orchestration.
• Ensure system reliability and performance across multiple security classifications and network types.
• Mentor engineers in the process of defining requirements with stakeholders and gathering acceptance.
• Communicate high-level technical trade-offs and implementation strategies to senior government stakeholders and Scale C-Suite members.
• Influence the long-term product strategy and technical roadmap for the Federal business unit.
• Consult on the architecture of AI-powered solutions for large-scale federal contracts.

Ideally you will have: 

• Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases
• Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
• Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
• AI Application Integration: Familiarity with integrating Large Language Models (LLMs) and building agentic workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration is beneficial.
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles
• Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment
• Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to defining and evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering

About Scale AI

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.

Role Overview

As a Forward Deployed AI Engineer on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers. You'll work with enterprise clients to understand their unique challenges, architect custom AI solutions, and ensure successful deployment and adoption of AI systems in production environments.

This is a hands-on technical role that combines deep engineering expertise with customer-facing problem solving. You'll work directly with customer engineering teams to integrate AI into their critical workflows.

Key Responsibilities

Customer Integration & Deployment

• Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements
• Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
• Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows
• Deploy and configure AI models and agents within customer security and compliance boundaries

AI Agent Development

• Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation
• Architect multi-agent systems that orchestrate between different models, tools, and data sources
• Implement evaluation frameworks to measure agent performance and iterate toward business objectives
• Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement

Prompt Engineering & Optimization

• Create sophisticated prompt engineering strategies optimized for customer-specific domains and data
• Build and maintain prompt libraries, templates, and best practices for customer use cases
• Conduct systematic prompt experimentation and A/B testing to improve model outputs
• Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate

Technical Leadership & Collaboration

• Serve as the primary technical point of contact for strategic enterprise accounts
• Collaborate with customer data scientists, ML engineers, and software developers to ensure smooth integration
• Provide technical training and knowledge transfer to customer teams
• Work closely with Scale's product and engineering teams to translate customer needs into product improvements
• Document technical architectures, integration patterns, and best practices

Problem Solving & Innovation

• Debug complex technical issues across the entire stack, from data pipelines to model outputs
• Rapidly prototype solutions to unblock customers and prove out new use cases
• Stay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems
• Identify opportunities for productization based on common customer patterns

Required Qualifications

• 4+ years of software engineering experience with strong fundamentals in data structures, algorithms, and system design
• Production Python expertise with experience in modern ML/AI frameworks (e.g., LangChain, LlamaIndex, HuggingFace, OpenAI API)
• Experience with cloud platforms (AWS, GCP, or Azure) and modern data infrastructure
• Strong problem-solving skills with the ability to navigate ambiguous requirements and rapidly iterate toward solutions
• Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences

Preferred Qualifications

Agent Development Wiz

• Deep understanding of LLMs including prompting techniques, embeddings, and RAG architectures
• Experience building and deploying AI agents or autonomous systems in production
• Knowledge of vector databases and semantic search systems
• Contributions to open-source AI/ML projects

Infrastructure Guru

• Experience with containerization (Docker, Kubernetes) and CI/CD pipelines
• Experience using Terraform, Bicep, or other Infrastructure as Code (IaC) tools
• Previous work in a devops, platform, or infra role
• Familiarity with enterprise security, compliance, and governance requirements (SOC 2, GDPR, HIPAA)

Customer Product Whisperer

• Proven ability to work with customers in a technical consulting, solutions engineering, or product engineering role
• Domain expertise in verticals like finance, healthcare, government, or manufacturing
• Experience with technical enablement or teaching programs

Sample Projects

The following are some examples of the types of projects we’ve worked on with customers. All of these projects leverage customer data, integrate directly into customers’ existing systems, and are deployed on their infrastructure.

Deep Research for Due Diligence

For a global professional services firm, we developed a sophisticated deep research agent to assist in due diligence. This agent employs a multi-agent architecture for robust fact-checking, integrates several internal MCP tools, and processes complex, unstructured data sources. This solution reliably saves employees hundreds of hours weekly.

Churn Prediction

Working with a TelCo organization, we built a model utilizing customer data to predict churn likelihood. The system then curates personalized offers based on this prediction. This model was integrated into a "next best action" copilot, enabling call center agents to proactively surface relevant offers to customers, leading to a significant reduction in churn.

Data Extraction Voice Agent

We partnered with a healthcare organization to create a lifelike voice agent and avatar designed to gather unstructured health information from patients. Engineered for low latency, the agent adeptly manages conversational flow, adheres to safety guardrails, and efficiently handles data extraction. This automation saves the organization's nurses hundreds of hours each week.

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.

About the General Agents Team

The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications. The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers.

About the Role

As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you’ll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle—from model and system design to evaluation, deployment, and iteration—bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments.

You will:

• Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases.
• Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints.
• Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings.
• Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs.
• Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems.
• Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage.
• Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level.

Ideally you’d have:

• 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases.
• Strong engineering fundamentals, supported by a Bachelor’s and/or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience.
• Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design.
• Proven proficiency in Python, including writing production-quality, testable, and maintainable code.
• Experience building systems that integrate models with external tools, APIs, databases, and services.
• Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.
• Strong communication skills and comfort working in customer-facing or cross-functional environments.

Nice-to-haves:

• Hands-on experience building AI agents using modern generative AI stacks (OpenAI APIs, commercial or open-source LLMs).
• Experience with agent frameworks, orchestration layers, or workflow systems (e.g., tool calling, planners, multi-agent setups).
• Familiarity with evaluation, monitoring, and observability for LLM-powered systems in production.
• Experience deploying ML systems in cloud environments and operating them at scale.
• Experience fine-tuning or adapting foundation models using methods like supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and low-rank adaptation (LoRA) to improve agent performance on domain-specific tasks.
• Interest in shaping the future of general-purpose enterprise agents and their real-world impact.

The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes:

• Create multi-layered guardrails around agents
• Optimize data retrieval for agents
• Orchestrate fleets of asynchronous agents
• Automatically alerts users to deviations in data
• Illustrating how an agent reached a decision

As a Senior Software Engineer, you will lead the development of a vertical feature or a horizontal capability to include defining requirements with stakeholders and implementation until it is accepted by the stakeholders.

You will: Lead the design and implementation of scalable backend systems and distributed architectures for Federal customers.

• Manage the full lifecycle of feature development from requirement definition to deployment on classified networks.
• Direct the orchestration of asynchronous agent fleets to meet mission requirements.
• Lead customer engagements to translate mission needs into technical requirements.
• Own the communication with stakeholders to ensure implementation meets defined acceptance criteria.
• Conduct technical reviews and identify risks within machine learning infrastructure and model serving.
• Drive the platform roadmap by providing technical specifications for Federal product offerings.

Ideally you will have: 

• Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases
• Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
• Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
• AI Application Integration: Familiarity with integrating Large Language Models (LLMs) and building agentic workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration is beneficial.
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles
• Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment
• Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to defining and evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering

Join the IT Engineering team as a Staff AI Enablement Engineer, where you will serve as the primary driver of AI enablement and solutions engineering across all departments at Checkr. This is a highly visible, hands-on role that blends deep technical expertise with a passion for teaching and empowering others. You will lead the charge on understanding team-specific requirements, configuring and evaluating AI tools, building bespoke AI-powered solutions, and standing up templatizable workflows that scale across the organization. From running office hours and hackathons to creating reusable resources and training materials, you will be the go-to person for helping every team at Checkr unlock the full potential of AI.

What you’ll do:  

• Requirements Gathering & Enablement: Embed with internal teams across departments to deeply understand their workflows, gather requirements, identify high-impact AI use cases, and translate business needs into actionable technical solutions.
• Lead Office Hours & Hackathons: Own and facilitate recurring AI office hours for drop-in support and guidance. Design and lead company-wide AI hackathons that inspire experimentation, knowledge sharing, and the discovery of innovative use cases.
• AI Tool Configuration & Evaluation: Evaluate, configure, and recommend the right AI tools and platforms for the organization. Stay on top of the rapidly evolving AI tooling landscape and make informed build-vs-buy recommendations.
• Build Bespoke AI Solutions: Design, develop, and deploy custom AI solutions tailored to specific team needs—leveraging LLMs, RAG architectures, agentic workflows, and automation frameworks to solve real business problems.
• Create Resources & Artifacts: Develop reusable playbooks, prompt libraries, workflow templates, how-to guides, and reference architectures that enable teams to self-serve on AI adoption.
• Templatize AI Workflows: Architect and implement standardized, templatizable AI workflows that can be adapted and deployed across multiple teams and use cases, driving consistency and scale.
• Training & Teaching: Lead hands-on training sessions, workshops, and demos to teach teams how to effectively use AI tools and integrate them into their day-to-day work. Act as a force multiplier by building AI fluency organization-wide.
• Cross-Functional Collaboration: Partner closely with Engineers, Business Systems, Security, Data Engineering, and other AI Solutions Engineers to integrate AI technologies into existing systems, ensuring security, compliance, and scalability.
• Strategic Planning & Roadmapping: Contribute to the AI strategy and roadmap at the organizational level, aligning initiatives with business priorities and ensuring AI investments deliver maximum value.
• Communicate & Evangelize: Present findings, demo solutions, share progress updates, and advocate for AI adoption with stakeholders ranging from individual contributors to executive leadership.

What you’ll bring:  

• Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience. A graduate degree is a plus.
• 5-7+ years of experience in engineering, solutions engineering, or technical enablement roles, with a track record of deploying solutions that measurably enhance productivity.
• 4+ years of hands-on experience developing AI-centric products and solutions, including machine learning, generative AI, LLMs, RAG architectures, agentic frameworks, and prompt engineering.
• Deep proficiency in Python and experience with AI frameworks and tools such as LangChain, LangGraph, LlamaIndex, OpenAI API, and similar; familiarity with containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure).
• Demonstrated ability to lead enablement programs—including running office hours, hackathons, training sessions, and creating scalable documentation and resources.
• Proven experience gathering requirements from non-technical stakeholders and translating them into well-scoped technical solutions.
• Exceptional ability to tackle open-ended, ambiguous problems in unstructured environments, synthesizing complex information into actionable plans and deliverables.
• Strong communication and presentation skills, with the ability to make complex AI concepts accessible to diverse audiences—from engineers to executives.
• A builder’s mindset: you are equally comfortable architecting a solution, writing production code, creating a training deck, and facilitating a workshop.
• A deep passion for AI and a genuine desire to help others harness its potential to transform how they work.

What you’ll get:

• A fast-paced and collaborative environment
• Learning and development allowance
• Competitive compensation and opportunity for advancement
• 100% medical, dental, and vision coverage
• Up to $25K reimbursement for fertility, adoption, and parental planning services
• Flexible PTO policy
• Monthly wellness stipend, home office stipend

#LI-TD1

The opportunity 

The Autonomous Freight Systems team is a brand new, AI-first engineering team in San Francisco. This team will own Flexport’s client-facing rates platform and the self-serve freight booking experience—two of the highest-leverage surfaces in our Client App that dictate how clients see pricing and book freight without manual intervention.

As a Staff Engineer, you will be the technical anchor for our next-generation AI-powered rates platform. We aren't just building a UI; we are building AI agents that handle real logistics work: parsing complex rate sheets, managing pricing intelligence across ocean, air, and trucking, and making "Self-Serve" a reality for thousands of shippers. You will build the intelligence layer that allows clients to commit freight on technology alone, with no account executive and no operations touch.

This is a ground-floor opportunity to shape technical direction, set up a new codebase, and build the platform that moves Flexport from an assisted-sales model to a tech-run one for the long tail of our client base. You will partner with Pricing, Sales, Ops, and Design on hard domain problems and ship to the most-used client surfaces at the company.

You will

• Design and ship end-to-end AI agents that handle pricing intelligence and booking compliance without human oversight.
• Create the tooling and secure AI infrastructure required to expose internal logistics data to LLMs safely.
• Navigate the rapidly evolving landscape of agent patterns, RAG, and prompt engineering to solve problems that don’t have "Stack Overflow" answers yet.
• Build and launch products rapidly and incrementally: we ship to production dozens of times per day.
• Mentor and uplevel engineers across the team, fostering a culture of technical excellence.
• Partner with the Engineering Manager to define the team’s technical roadmap and hiring bar.

You should have

• LLM Fluency: Experience with agent patterns, RAG, and prompt engineering. You know what works in production versus what only looks good in a demo.
• Agentic Orchestration: Experience with workflow orchestration (e.g., Temporal) and building reliable systems around non-deterministic AI.
• Entrepreneurial Drive: You don't wait for a spec; you find the highest-leverage automation problems and design the AI-driven solution.
• Product Instincts: You can identify what is worth building, not just execute on a spec. You have a proven track record of building data-heavy applications where high-quality user interaction is the priority.
• Architecture Leadership: Demonstrated experience leading technical initiatives across multiple teams or systems, specifically in building unified interfaces that aggregate data from various backend systems.
• Technical Rigor: A love of simple, well-tested code that is easy to extend and hard to break, combined with experience building large-scale distributed systems.
• Technical Communication: The ability to convey technical ideas in common terms to business partners, product managers, and engineers alike.

It's a plus if you have

• Experience with elements of our tech stack: Java, Spring Boot, Ruby on Rails, React, AWS.
• Experience in logistics, supply chain, or freight technology.
• Experience building internal tools or operator-facing applications.
• A bias for action: When you see a problem, you solve it.

#LI-onsite

About the role

Why this role is exciting

Why you'll love it here:

• 💰 Competitive Compensation. For this role our salary is targeted at $105-$140k USD per year. Final offer amounts are determined by factors such as experience and expertise. We take a geo neutral approach to compensation within the US, meaning that we pay based on job function and level, not location. 
• 🏆  Syndio Equity. So you can share in Syndio’s success.
• 🏝  Flexible Vacation Policy. We encourage our team to recharge when they need to, plus paid sick & safe time, compassion leave, parental leave, and voting leave. 
• 🩺  Medical, Dental, Vision. Syndio pays 90% of employee premiums, and 50% for dependents.
• 🏥  Life Insurance & Disability. Syndio covers the full premium.
• 🏦 401(k). To help you save for your future
• 📍 Remote-first in the following approved U.S. states: AZ, CA, CO, CT, DC, FL, GA, ID, IL, IA, MD, MA, MN, NE, NJ, NY, OH, OR, PA, RI, SC, SD, TX, UT, VT, VA, WA, and WI #LI-Remote

Role Progression:

In the 1st Month:

• Complete onboarding on the Syndio platform and the "Decision Intelligence" vision.
• Master the new Integration Platform and current data loading artifacts.
• Shadow 2-3 strategic client implementations and identify potential automation opportunities.
• Contribute to a pilot migration of an existing client’s data into the new platform.

At 3 Months:

• Independently lead technical implementations for new clients.
• Execute data migrations for a segment of the existing client base into the new Integration platform.
• Build reusable "Implementation Artifacts" (scripts, prompts, templates) that reduce time-to-value for the whole team.
• Contribute to the design of our new Platform’s Implementation process, identifying automation and other artifacts to minimize the client’s time to value.

By 6 Months:

• Manage a portfolio of high-complexity implementations for Syndio’s most strategic accounts utilizing automation and other artifacts to maximize client’s value in minimum time.
• Help achieve the goal of migrating 70% of the client base to the new Integration platform solution.
• Serve as a subject matter expert for "Process Embedding," ensuring clients are fully adopting the platform’s advanced setup features.

The interview overview

Below you'll find an outline of the interview plan for our Forward Deployed Engineer position. Please note that this is what we expect the process to look like; we may ask you for supplemental information or require an additional step before making a final decision.

1. 30 min phone call with a member of our Talent Team.
2. 45-60 min Zoom deep dive with the Hiring Manager (Director of Forward Deployed Engineering).
3. 75 min hands-on technical interview with a Senior Engineer.
4. 2-3 video interviews with key members of the Customer Success and Product teams.
5. 30 min interview with the SVP of Engineering - Data Strategy.

The role:

SoFi’s Staff AI Engineer is a highly experienced, hands-on individual contributor within SoFi’s growing independent risk organization, focused on owning the design, development, and evolution of agentic AI systems to solve real-world, high-impact problems.

This role will be instrumental in architecting, building, and scaling AI systems that enhance risk management and internal workflows, with a focus on creating reliable, reusable, and production-grade solutions.

This role operates at the intersection of the intelligence layer, including LLMs, agents, and orchestration, and the experience layer, which defines how users interact with and derive value from AI systems. You will shape how these systems are designed and integrated into critical workflows, ensuring they are intuitive, reliable, and effective in high-stakes risk environments.

You will work closely with the Senior Manager of AI Engineering as well as business stakeholders, to translate complex, ambiguous problems into scalable, production-grade AI systems with measurable impact.

What you’ll do: 

• Architect and Develop Agentic AI Systems: Lead the design and development of AI systems that leverage multi-step reasoning, tool use, and structured workflows, using frameworks such as LangGraph or similar approaches. Incorporate planning, memory, tool integration, and adaptive control flow to enable automated decisioning, risk insights, and internal platforms.
• Design the Experience Layer: Define how users interact with AI systems by designing workflows, interfaces, and feedback loops that drive adoption, usability, and trust. THis will involve close coordination with users / stakeholders. Ensure alignment between system behavior and user expectations.
• Context Engineering and System Design: Define and implement approaches for structuring inputs, outputs, and system context to improve reliability and performance of LLM systems, including prompt design, retrieval strategies, and workflow composition.
• Productionize AI Systems: Develop production-grade services and APIs, integrate agents into real systems, and ensure scalability, reliability, and maintainability.
• AI Observability and Evaluation: Build tracing, debugging, and evaluation frameworks to understand system behavior and continuously improve agent performance.
• Cross-Functional Collaboration: Partner with risk, engineering, and business teams to translate ambiguous problems into working AI systems and deliver measurable outcomes.
• Proof of Concepts and Innovation: Innovation and Prototyping: Identify high-impact opportunities to apply AI, rapidly prototype solutions, and evaluate emerging tools and approaches to inform long-term system design aligned with latest trends in AI.

What you’ll need:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field.
• 7+ years of software engineering experience, with significant experience building and scaling AI-powered systems in production.
• Strong experience working with LLMs and building applications using prompting, APIs, and/or agent frameworks.
• Experience designing and implementing agentic systems, including patterns such as tool use, multi-step reasoning, and workflow orchestration.
• Deep experience in context engineering for LLM systems, including structuring inputs and outputs, prompt design, and retrieval-based approaches.
• Strong backend engineering experience, including building scalable services and APIs (Python preferred).
• Experience designing systems on cloud platforms such as AWS, Azure, or GCP, with an understanding of modern development and deployment practices.
• Experience working with structured and unstructured data, including building pipelines to support downstream AI applications.
• Experience defining and implementing evaluation frameworks for AI systems, including metrics, experimentation, and performance iteration.
• Strong system design skills, with the ability to architect scalable, reliable solutions.
• Ability to operate effectively in ambiguous problem spaces and translate them into well-defined systems.
• Strong communication and collaboration skills, with the ability to work cross-functionally and influence technical decisions.
• Demonstrated ownership mindset, with a track record of delivering high-impact systems end-to-end.

Nice to have:

• Experience designing and building user-facing workflows or internal tools powered by AI.
• Familiarity with observability and evaluation tools for AI systems such as Langfuse, LangSmith, or similar.
• Experience working in financial services or building systems for risk-related use cases.
• Experience with frontend technologies such as React for building AI-powered interfaces.
• Experience contributing to shared platforms, libraries, or internal tooling that enable reuse across teams.
• Experience building systems that require explainability, auditability, or operate in regulated environments.

This is Engineering at Lattice

At Lattice, we build software that helps people and organizations thrive. Our engineering team values maintainable systems, strong collaboration, and thoughtful product experiences.

As we expand AI-powered capabilities across our platform, security is a core part of how we design, build, and operate our product. Our Product Security team partners closely with engineering to ensure we ship features that are secure by default and resilient in production.

What You Will Do

You will work closely with product and platform engineering teams to improve the security of Lattice’s applications and services.

Product Security & Engineering Partnership

• Partner with engineers to identify, triage, and remediate security issues in product features and services
• Participate in security reviews and threat modeling for new features and systems
• Perform security-focused code reviews and help identify common vulnerabilities
• Contribute to secure-by-default patterns, libraries, and tooling in our TypeScript-based stack

Security Tooling & Operations

• Help implement and operate security tooling (SAST, DAST, dependency scanning, etc.)
• Support vulnerability management workflows, including internal findings and bug bounty reports
• Assist in investigating security issues and assessing risk and impact

Cloud & Platform Security

• Collaborate with platform and infrastructure teams to improve application and cloud security posture
• Contribute to improving security practices in AWS-based environments

AI/LLM Security

• Assist in identifying and mitigating risks in AI/LLM-powered features, including prompt injection, data leakage, and unsafe output handling
• Apply emerging best practices (OWASP Top 10 for LLM Applications) to real product use cases

Security Enablement

• • Contribute to security guidance, documentation, and training for engineering teams
  • Help improve how security is integrated into the development lifecycle

What You Will Bring to the Table

Experience that is important for you to have at some level:

• 1–3+ years of experience in product security, application security, or software engineering
• Experience writing and maintaining code in JavaScript/TypeScript (or similar languages like Python or Ruby)
• Familiarity with common web and API vulnerabilities (e.g., OWASP Top 10)
• Exposure to security testing tools (SAST, DAST, dependency scanning, etc.)
• Experience working in or with cloud environments (AWS or similar)

Technical Approach

• Ability to identify common security risks and suggest practical mitigations
• Understanding of secure coding practices and basic security controls
• Interest in how security decisions impact real-world product systems

Collaboration & Growth

• Strong communication skills and ability to work closely with engineering teams
• Willingness to ask questions, learn quickly, and take ownership of well-scoped problems
• Ability to contribute to team discussions and share security context effectively

Experience that would be nice to have:

• Experience with modern web architectures (Next.js, NestJS, GraphQL)
• Familiarity with containerization or Kubernetes
• Experience or interest in AI/LLM security, including

----

The estimated annual cash salary for this role is $116,500 - $154,000. This position is also eligible for incentive stock options, subject to the terms of Lattice’s applicable plans

Benefits: The Company offers the following benefits for this position, subject to applicable eligibility requirements: Medical insurance; Dental insurance; Vision insurance; Life, AD&D, and Disability Insurance; Emergency Weather Support; Wellness Apps; Paid Parental Leave, Paid Time off inclusive of holidays and sick time; Commuter & Parking Accounts; Lunches in the Office; Internet and Phone Stipend; 401(k) retirement plan; Financial Planning; Learning & Development Budget

*Note on Pay Transparency:

Lattice provides an estimate of the compensation for roles that may be hired as required by state regulations. Compensation may vary based on (a) location, as Lattice factors in specific location when benchmarking compensation for most roles; (b) individual candidate skills and qualifications; and (c) individual candidate experience.

Additionally, Lattice leverages current market data to determine compensation, so posted compensation figures are subject to change as new market data becomes available. The salary, other compensation, and benefits information is accurate as of the date of this posting. Lattice reserves the right to modify this information at any time, subject to applicable law.

#LI-remote

What we do  

You’ll join a software engineering team building internal AI solutions that support teams across The Trade Desk. We leverage large language models, retrieval‑augmented generation (RAG), agentic systems, and enterprise platforms like Microsoft Copilot and Anthropic Claude to improve productivity, streamline workflows, and reduce operational friction.

Our team also plays a key role in AI enablement across the company—supporting teams as they adopt Copilot, Claude and similar tools, designing and deploying custom AI agents, and guiding partners through their AI journey from idea to impact.

As a Senior AI Engineer on this team, you’ll be an end-to-end owner—collaborating closely with stakeholders, identifying opportunities, and delivering tools that make a real impact. We’re a nimble, fast-paced, and highly collaborative group that thrives on transforming the way teams operate and driving greater value across The Trade Desk.

What you’ll do: 

Some of the work that you will be doing to help us deliver on our mission is:

• Design and deploy intelligent agentic systems that integrate large language models (LLMs) with enterprise data, tools, and workflows using frameworks like LangChain, LlamaIndex, and Semantic Kernel.
• Develop Retrieval-Augmented Generation (RAG) applications using tools like Azure AI Search, vector databases, and secure enterprise connectors to deliver contextual insights.
• Build and deploy agents using Microsoft Copilot, Copilot Studio, Anthropic Claude, and similar platforms to help teams operationalize solutions within enterprise guardrails.
• Build and iterate on conversational agents that solve real-world problems, meet stakeholder needs, and deliver measurable business value.
• Deliver high-impact features by collaborating across teams, leading through ambiguity, and aligning technical solutions with business goals.
• Drive quality and performance through automated testing, monitoring, and data-driven evaluation of success criteria, user adoption, and operational efficiency.
• Mentor teammates and contribute to a culture of innovation, technical excellence, and continuous learning.

Who you are: 

• You have a Bachelor’s/Master’s level degree in computer science or relevant engineering-related field or equivalent experience.
• You have 8-10+ years of software engineering experience, including 1-2+ years working on AI-powered systems or products.
• You are proficient in Python and comfortable with additional languages such as C#, SQL, or TypeScript/React.
• You’re excited about AI enablement, including helping teams adopt platforms like Microsoft Copilot, Anthropic Claude and Copilot Studio, building agents, and guiding others through their AI journey.
• You have a good foundational knowledge of or hands-on experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel) and prompt engineering using LLM APIs.
• You have experience productionizing applications and implementing CI/CD pipelines to ensure reliable, scalable deployments.
• You are skilled in data ingestion and transformation using APIs, ETL pipelines, and connectors, and familiar with vector databases and retrieval strategies.
• You communicate clearly across technical and non-technical audiences and thrive in a collaborative, cross-functional environment.
• You are a fast learner who adapts quickly to new technologies and solves complex problems with creativity and pragmatism.

#LI-TP1

A variety of technical opportunities is one of the best things about working at The Trade Desk as a software engineer, which is why we do not expect you to know every technology we use when you start. What we care about is that you can learn quickly and find solutions to complex problems using the optimum tools for the job. What you know is less important than how well you learn and innovate. We are not seeking engineers who know all the answers; we need engineers who can invent answers no one has thought of yet and find answers to the questions yet to be asked. 

The Trade Desk does not accept unsolicited resumes from search firm recruiters. Fees will not be paid in the event a candidate submitted by a recruiter without an agreement in place is hired; such resumes will be deemed the sole property of The Trade Desk. The Trade Desk is an equal opportunity employer. All aspects of employment will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

[LA JOBS ONLY] The Trade Desk will consider qualified applicants with criminal histories for employment in a manner consistent with the requirements of the Los Angeles Fair Chance Initiative for Hiring, Ordinance No. 184652.

[SF JOBS ONLY] Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

#LI-TP1

Software Engineer (Full Stack), San Francisco Hybrid

FT Partners is looking for a skilled Software Engineer who is knowledgeable in building high quality impactful applications and well versed in AI development methodologies. We are looking for people who can produce stable features without racking up technical debts, and can help organize, consolidate, and strengthen our suite of software for sustained impact.

The software to which you will contribute is FT Base, an internal project management and data application suite, built in Python, Django (DRF), Typescript, React, PostgreSQL. FT Partners runs all its operations on FT Base, and while its userbase is limited to company employees, it’s a mission critical application with usage around the globe. It also powers an MCP server to provide data and interactivity to employees through enterprise AI tooling.

Qualifications

• Experience requirements, either:
  • 5 years as a Software Engineer in a web application environment OR
  • 2 years as a Software Engineer and reputable boot camp certification OR
  • Engineering degree (4 year) and some projects or work experience to show
• Python, Typescript, APIs, Git and web application system design concepts

AI Qualifications

• Strong A.I. prompt-driven development abilities, backed by technical understanding
• You don’t need to know how to have hand-coded everything you produce, but you need to be able to explain and verify any ai-produced code you submit.
• Experience overcoming the obstacles that lie beyond the prototype, and instinct toward cohesive organized systems that scale

Key Responsibilities

• Immerse yourself in the context and business requirements of each of our system’s many functional areas
• Execute on instructions to build new things, or improve, refactor, clean up or delete features
• Make recommendations on progressing projects you are close to, sometimes in growth, sometimes in simplification, sometimes in aligning with other areas of the system
• Help review applications prototyped outside of the engineering department, and help steer, rebuild, or secure these

Ideal Mindset

• Responsible and organized. We are not looking for someone to break ground and hand off their problems, we are looking for someone to help funnel ideas into sustainable compatible features
• Attentive to detail
• Systems minded
• Flexible to business pressures and constraints.
• Collaborative and team-oriented, understanding the importance of shared success and learning
• Open to asking questions, sharing ideas, helping teammates
• Eager to adopt new standards and improve upon previous solutions
• Cautious of adding unnecessary complexity. And cognizant of maintenance burdens that may be introduced by flashy features. We want the features. But let’s make them robust and compatible
• Committed to stability through test coverage and writing documentation

Nice to Have

• AWS / Cloud Infrastructure
• Django / Django Rest Framework (DRF)

Why Choose FT Partners?

• Leadership from Managing Partners and Managing Directors with backgrounds at Goldman Sachs and JP Morgan.
• Award-winning execution, recognized as Investment Bank of the Year and Boutique of the Year, among other accolades.
• Involvement in both billion-dollar transactions and early-stage, high-growth VC deals.
• Clear career progression and competitive compensation packages.
• Access to an unparalleled deal flow with a distinguished client base.
• Opportunities to engage with world-class executives in a high-growth sector.
• Established relationships with top financial sponsors.
• A close-knit, rapidly growing team environment.

Selected Transactions Include Advising

• Divvy's $2.5 billion Sale to Bill.com
• GreenSky’s $2.2 billion Sale to Goldman Sachs
• Forge’s $2 billion Merger with Motive Capital Corp.
• Truebill’s $1.3 billion Sale to Rocket Companies
• Revolut's $800 million Financing at a $33 billion Valuation
• SpotOn’s $300 million Series F Financing at a $3.6 billion Valuation
• Upgrade’s $280 million Series F Financing at a $6 billion Valuation
• CAIS’ $225 million Series C Financing at a $1 billion+ Valuation
• Bilt’s $200 million Financing at a $3.1 billion valuation
• Newfront’s $200 million Financing at a $2.2 billion Valuation
• DailyPay’s $75 million Financing at a $1.8 billion Valuation
• Cart.com’s $60 million Series C Financing at $1.2 billion Valuation
• Acorn’s Acquisition of GoHenry

Inclusion and Equal Opportunity Employment: Financial Technology Partners is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veterans status, Aboriginal/Native American status or any other legally-protected factors. If you require reasonable accommodation as part of the application process, please contact recruiting@ftpartners.com

At Qualia, we've built the leading B2B real estate technology that transforms the home buying and selling experience into a simple, secure, and enjoyable process. Our SMB and Enterprise products bring together users from across the real estate ecosystem---homebuyers and sellers, lenders, title and escrow agents, and real estate agents---onto a single shared digital closing platform, providing greater clarity and transparency to real estate transactions. Today, through our business customers across the country, millions of consumers use Qualia to close on homes every year.

WHAT YOU'LL WORK ON

We are hiring an entrepreneurial Engineering Manager to lead Qualia's Application Security team. This is a builder's role. You won't just run a team - you'll redesign how a modern AppSec function operates when AI can do the first pass on nearly everything we used to do by hand.

The team today owns secure design reviews, vulnerability triage, internal penetration testing, incident response support, and security tooling across a JavaScript/NodeJS and Kubernetes stack. Your mandate is to scale that surface area vertically - growing output and coverage per engineer - by making AI-assisted workflows the default. That means automated pen testing pipelines, AI-driven triage of findings from SAST/DAST/SCA, agentic review of engineering proposals and design docs, and continuous red-teaming exercises that test both our systems and our assumptions.

You'll partner closely with Platform, Infra, and product engineering leaders to embed security earlier in the development lifecycle, and you'll be the team's voice when we set the security vision for the next two years - including anomaly detection across production traffic, model-driven threat hunting, and how we defend against (and responsibly use) AI-enabled attackers.

Securing that platform - the money, the identities, and the documents flowing through it - is what the Application Security team does every day. We're hiring an Engineering Manager to lead this team into its next chapter: one where AI is a force multiplier for every part of our security program.

RESPONSIBILITIES

• Lead and grow the Application Security team - coaching senior AppSec engineers, setting goals, and owning delivery against the security roadmap
• Build the automated pen-testing program. Stand up pipelines that run continuous, AI-assisted offensive testing against our services, APIs, and web properties - and turn the output into a triaged, actionable queue
• Scale triage with AI. Design the workflows and tooling that let the team handle 10x the volume of findings (bug bounty, scanner output, customer reports) without 10x the headcount
• Review engineering proposals. Sit at the front of the design process with engineering leaders across Core, Clear, Shield, Connect, and Atlas - reviewing RFCs and proposals, flagging risk early, and helping teams ship securely by default
• Run red-teaming exercises. Drive recurring red team engagements - both internal exercises and coordinated vendor work - and close the loop into detection, response, and product hardening
• Own the AppSec vision. Partner with the leadership team to set multi-quarter strategy across anomaly detection, threat modeling, and AI-augmented defense
• Fight fires when they happen. Lead incident response from the application security side, and be the person engineering trusts to make the call in the room
• Mentor and hire. Recruit strong AppSec engineers, mentor the ones you have, and build a team culture where people are pushed and supported in equal measure

YOUR BACKGROUND THAT LIKELY MAKES YOU A MATCH

• 5+ years as a security or full-stack engineer working on production systems, with 2+ years managing a security or platform engineering team
• Hands-on depth in application security: threat modeling, code review, and at least one offensive-security discipline (pen testing, red team)
• Track record of shipping automation that changed how a team worked - ideally including meaningful use of LLMs, agents, or ML in a security or engineering workflow
• Comfort operating across the full security lifecycle: prevention, detection, response, and recovery
• Strong written communication. You can write the design doc, the post-mortem, and the board-ready summary - and you can tell a product engineer why their proposal needs to change without shutting down the conversation
• Keen product sense and a bias toward measurable impact. You care whether the risk actually went down, not whether a ticket got closed
• Experience in fintech, real estate tech, or another regulated, high-liability domain preferred

NICE TO HAVE

• Background designing or operating anomaly-detection systems on production traffic, auth logs, or financial transactions
• Published research, CVEs, or conference talks in AppSec, offensive security, or AI security.
• Familiarity with the evolving landscape of AI-enabled offense (prompt injection, model abuse, agent exploitation) and defense

THE TECH STACK YOU'LL USE

• JavaScript / NodeJS / Typescript  / Meteor
• Microservice architecture / Kubernetes Operators
• AWS
• MongoDB
• Modern AppSec tooling: SAST/DAST/SCA and an expanding AI-assisted security automation layer that you'll help build

While this role is remote work eligible, we have three office locations: San Francisco, California; Concord, New Hampshire; and Austin, Texas.

This role has a base annual salary of $210,000-$240,000 plus a competitive equity and benefits package. (Salary to be determined by relevant experience, location, knowledge, and skills of the applicant, internal equity, and alignment with market data.)

WHY QUALIA

Qualia is made up of incredibly bright, mission-driven coworkers who are passionate about using technology to solve real-world problems---and we're growing quickly. In order to continue building an engaging and dynamic organization, we're committed to giving everyone the support they need to do great work.

Our benefits package is designed to allow our team members to be their best selves, both in and out of the workplace. In addition to comprehensive health plans, a 401k program, and commuter benefits, we prioritize family and personal well-being through professional development, parental leave, and a flexible time off policy. Qualia offers a robust online onboarding program to train new hires, biweekly all hands meetings, and a variety of internal virtual events to keep employees connected.

We believe diverse perspectives and backgrounds are critical to building great technology, and our goal is to cultivate an environment where people feel equally valued and respected. Qualia is proud to be an equal-opportunity workplace, and we welcome applicants from all backgrounds regardless of race, color, ancestry, religion, gender identity or expression, sexual orientation, marital status, age, citizenship, socioeconomic status, disability, or veteran status.

By submitting your application, you acknowledge and agree to the collection, processing, and use of your personal information as described in our Employee Data Privacy Notice.

#LI-Remote

The Role

As an Applied AI Marketing Engineer, you will be a core, hands-on builder within the new Marketing AI Spoke team. You will function as the primary product owner and technical implementer for a specific portfolio of AI agents, workflows, and low-code/no-code applications. This role requires a blend of marketing acumen, technical aptitude, and problem-solving skills to translate complex business challenges into tangible, deployed, AI-driven solutions.

You will be responsible for the full engineering lifecycle—defining, developing, testing, and deploying high-impact initiatives that directly improve our marketing productivity, operational efficiency, and buyer experiences.

Responsibilities

• Marketing AI Solution Development: Patterning with TDI leveraging company-wide approved platform. Design, build, and maintain the underlying infrastructure and services required to run scalable AI applications for the Marketing organization. This includes connecting data sources, building APIs, developing front-end user interfaces (UI/UX), and engineering the backend logic.
• AI Agent & Workflow Engineering: Architect, code, test, and deploy AI agents and low-code/no-code workflows that solve critical marketing challenges. Ensure solutions are robust, secure, and seamlessly integrated into the existing AI stack (e.g., CRM, MAP, CMS).
• Agentic Strategy & Architectural Design: Act as the technical leader for your assigned portfolio, defining the architectural blueprints for new AI enabled workflows. Evaluate and recommend the appropriate technical tooling (e.g., cloud services, databases, low-code platforms) to maximize performance and minimize operational costs.
• Data and Prompt Engineering: Partner with data science and marketing ops teams to ensure data pipelines support AI application needs (RAG, fine-tuning). Design and iterate on effective prompts and input-output schemas to optimize the performance and reliability of large language model (LLM) agents.
• Value Measurement & System Optimization: Implement performance tracking directly into deployed platforms (instrumentation). Use technical metrics (latency, uptime, error rates) and business metrics (ROI, time saved) to justify system improvements and guide the marketing AI roadmap. 

What You'll Bring

• A Bias for Action and Iteration: Demonstrated passion for a test-and-learn philosophy, quickly prototyping and validating solutions to drive continuous improvement.
• AI-First Technology Mindset: Deep curiosity and commitment to leveraging AI and machine learning as foundational elements for building next-generation marketing tools and operational workflows.
• Marketing Domain Curiosity: A strong interest in marketing dynamics, customer behavior, and business outcomes, translating technical capabilities into tangible marketing value.
• Preferred Experience (A Strong Plus): Prior background in marketing operations, strategy, or experience working on tools/platforms utilized by large-scale marketing organizations.

Why You'll Love This Role

• Become the Architect of Change: You won't be maintaining existing systems; you will be building a new, high-impact function from the ground up. This is a chance for true architectural ownership over the next generation of our marketing operating model.
• Tangible and Immediate Impact: Your work on groundbreaking AI projects will have a direct, measurable impact on our business performance, customer experience, and efficiency. You will see your innovations adopted by hundreds of marketers and contribute to trillions in potential value.
• High-Visibility, Strategic Ownership: This role offers executive-level visibility as you directly shape the future of marketing strategy at Okta, moving the function from manual repetition to hyper-efficient, data-driven automation.

#LI-Hybrid
PID: P24412_3380938

The Auth0 Team

Auth0 is an easy-to-implement authentication and authorization platform designed by developers for developers.  We make access to applications safe, secure, and seamless for the more than 100 million daily logins around the world. Our modern approach to identity enables this Tier-Ø global service to deliver convenience, privacy, and security so customers can focus on innovation.

This team focuses on providing tenant-level protections to our customers, at scale. From bot detection to brute-force to suspicious IP throttling and beyond, this team often provides the first line of defense for Auth0 customers.

The Staff Software Engineer 

At Okta, we’re building the next generation of authentication for the GenAI era. 

We’re looking for a Staff Software Engineer to join the AI DevEx team at Auth0. This role is pivotal in extending and complementing our Auth for GenAI offering by building the infrastructure, tooling, and developer experiences that empower both human developers and AI agents to build secure, intelligent applications.

Auth0 Emerging Tech is the Engineering organization where we take care of the hottest technology out there: we ship fast, we don't break things. We are a dynamic and collaborative distributed and diverse team. We value ownership, learning and innovation.  

This is an ideal role for an engineer who enjoys building for other engineers, working across stacks, and shaping the future of AI enablement in production systems. You'll collaborate across engineering, product, and security teams to drive meaningful improvements in developer tooling, agent authentication, orchestration frameworks, and real-world demos.

What you will be doing:

• Design and Build Developer Tooling that helps developers secure and manage infrastructure like MCP servers
• Build Demo Applications that showcase secure, identity-powered AI use cases in real-world environments
• Contribute to Open Source Projects, both within Auth0 and across the broader AI + identity ecosystem
• Write and Maintain High-Quality Documentation including API references, quickstarts, and best practices for both developers and AI-native tooling (e.g., llm.txt)
• Drive Integration with Emerging AI Frameworks by creating adapters, utilities, and interfaces for agent runtimes and orchestration layers
• Collaborate with Design, Product, and Security teams to align on developer needs, roadmap direction, and compliance requirements
• Mentor and Support Other Engineers, setting strong examples in code quality, testing practices, and architectural thinking
• Influence Engineering Standards by leading design discussions and contributing to team-wide architectural decisions
• Ensure Resilience and Security of systems involved in agent-to-agent or model-to-service communication

You Might Be a Good Fit If You

• Experience in software engineering with a proven track record in building tools, frameworks, or platforms for other developers
• Proficiency in JavaScript/TypeScript, Golang and/or Python, and the ability to move fluidly between front-end and back-end contexts
• Experience working with LLM APIs, agent runtimes, orchestration layers, or prompt pipelines
• Familiarity with authentication and authorization systems, especially standards like OAuth2, OIDC, and JWT
• Demonstrated experience leading architecture and design efforts for scalable, production-grade systems
• Comfort contributing to and maintaining open source projects and engaging with developer communities
• A passion for documentation as part of the developer experience—not just writing code, but making it understandable and usable
• Ability to thrive in highly collaborative environments with cross-functional stakeholders

Technologies You May Work With

• Languages: JavaScript, TypeScript, Python
• Frameworks: React, Next.js, FastAPI
• AI Ecosystem: Model APIs, orchestration runtimes, prompt management systems, agent toolkits
• Auth0 Stack: Token Vault, Async Authorization, Fine-Grained Authorization (FGA)

#LI-Hybrid

P23578_3268067

AI Engineer
Location: San Francisco, CA, in-office
Compensation: 150,000 – $210,000
Company: Cargomatic, Inc.

Disruptive, groundbreaking, transformative company is changing an industry

Join a rapidly growing company disrupting the trucking industry! Cargomatic is the #1 technology platform and digital marketplace for powering world-class, local trucking. Take a look around you. Literally everything humans build, grow, or sell has spent time on a truck. Local trucking is the lifeblood of every regional economy, and yet this $82 billion industry still relies heavily on phone calls and fax machines. Cargomatic is transforming the way goods move around every local node in the supply chain by connecting shippers and commercial truck drivers with mobile technology. We are solving complex, real-world problems every day, and giving full transparency to the shipping process. 

AI Engineer- Rapid Prototyping & Automation

Cargomatic is seeking a highly hands-on AI Engineer who thrives in fast-paced environments and is passionate about rapid prototyping and deploying standalone AI-driven applications. This role focuses on building agentic AI systems and automation tools that can operate independently, integrate with our ecosystem, and deliver immediate business value.

You will work at the intersection of AI, product development, and infrastructure- quickly turning ideas into working prototypes and scaling the most promising solutions into production.

Key Responsibilities:

• Rapidly design, prototype, and deploy standalone AI applications that solve real-world logistics and operational challenges
• Build and implement agentic AI systems capable of autonomous decision-making and task execution
• Develop AI-powered automation tools to streamline workflows across operations, customer experience, and internal processes
• Leverage modern LLM ecosystems, including Claude-based and code-first AI frameworks, to build intelligent agents and workflows
• Integrate LLMs, APIs, and data pipelines into production-ready services
• Collaborate with product, engineering, and operations teams to identify high-impact opportunities for AI solutions
• Iterate quickly based on feedback, performance metrics, and evolving business needs
• Ensure scalability, reliability, and security of AI systems in production environments
• Stay current with advancements in AI/ML, especially in agent frameworks, Claude/OpenAI ecosystems, and automation tooling (e.g., n8n)
• Implement guardrails, validation layers, monitoring, and evaluation frameworks to mitigate hallucination, prompt injection, and data security risks.
• Collaborate cross-functionally with Product, Engineering, and Operations to translate business needs into scalable AI-enabled solutions.
• Contribute to architectural standards, technical best practices, and innovation initiatives.

Preferences:

• Strong product builder mindset with an ability to prioritize high-impact solutions
• 3+ years of experience in software engineering, with a focus on AI/ML or automation
• Proven experience building and deploying AI-powered applications or prototypes end-to-end
• Strong understanding of agentic AI concepts, LLMs, and modern AI frameworks
• Hands-on experience with Claude (Anthropic), OpenAI, or similar LLM APIs, including prompt design and orchestration
• Experience with code-based AI frameworks (e.g., Claude tool use / function calling, LangChain, AutoGen, CrewAI, or similar)
• Experience building or orchestrating workflows using n8n (or similar tools like Zapier, Temporal, Airflow)
• Experience with event-driven architectures and real-time systems
• Familiarity with vector databases and retrieval-augmented generation (RAG)
• Proficiency in backend development using Node.js
• Experience with frontend frameworks such as React for building lightweight interfaces
• Hands-on experience with cloud infrastructure, particularly AWS (Lambda, ECS, S3, etc.)
• Familiarity with databases such as MongoDB and RDS (PostgreSQL/MySQL)
• Experience integrating third-party APIs and building microservices
• Ability to move quickly from concept → prototype → production
• Active GitHub contributions or demonstrated passion for emerging technologies in AI and data systems.

Benefits & Perks:

• Competitive compensation
• Medical, dental, and vision benefits
• 401K company match program
• Flexible paid time off (PTO) and paid holiday

The Safety Processing team is responsible for the systems that power Discord's ability to detect, review, and enforce against harmful content at scale. We build the infrastructure and decision systems that enable accurate, efficient, and fair content moderation across all of Discord. We're looking for a Senior Software Engineer who can handle complex, multi-milestone projects and deliver high-quality systems that protect millions of users daily.

As a Senior Engineer on Safety Processing, you'll take full ownership of projects from initial design through post-launch monitoring and iteration. You'll work on challenging problems at the intersection of automation, large-scale distributed systems, and content safety. You'll help build automated decision systems that scale our review capacity by 10x, develop enforcement infrastructure that handles millions of decisions per day, or architect systems that centralize and simplify safety signal processing. You'll collaborate closely with Trust & Safety operations, ML teams, Policy, and product partners to deliver solutions that make Discord safer while maintaining our commitment to user experience. You'll deliver lovable products while maintaining Discord's high quality bar, utilizing 80/20 thinking and a user-centric approach. You'll embody a growth mindset, diving into new code and technologies to deliver safety solutions that protect millions of users daily. This person reports to the Engineering Manager of Safety Processing.

This role offers the opportunity to work on systems that directly impact user safety, tackle novel technical challenges in content moderation at scale, and help shape the future of how Discord keeps communities safe.

What You'll Be Doing:

• Own complex safety infrastructure projects end-to-end, from initial design through post-launch monitoring and iteration
• Build and maintain critical systems for automated content review, enforcement, and safety signal processing at massive scale
• Apply an adversarial mindset to your work, thinking through security, abuse scenarios, edge cases, and scalability concerns to protect Discord's users
• Collaborate with Trust & Safety, ML, Policy, and product teams to understand requirements and deliver effective safety solutions
• Break down complex, multi-milestone projects into smaller work streams while managing stakeholders and dependencies across teams
• Debug and resolve issues within the team's codebase and adjacent systems, participating in on-call rotations
• Contribute to technical design discussions and documentation, writing RFCs
• Help establish best practices and maintain the team's quality bar through code reviews and mentorship

What You Should Have:

• 4+ years of professional software engineering experience with a focus on backend services and infrastructure
• Strong programming skills in Python
• Experience building and operating distributed systems and production services at scale
• Demonstrated ability to own projects end-to-end and deliver results on time
• Strong debugging skills and ability to navigate complex codebases
• Excellent collaboration skills and ability to work effectively with cross-functional partners
• Experience working in larger engineering organizations with multiple teams and stakeholders
• A quality-centered mindset with attention to the right details at every stage of development
• Demonstrated adversarial thinking - ability to anticipate misuse, edge cases, and system vulnerabilities
• Experience with distributed systems concepts and cloud platforms (GCP/AWS)
• Experience being in 24/7 on-call rotations
• Strong collaboration and communication skills

Bonus Points:

• Experience with content moderation, trust & safety, or related problem domains
• Experience with workflow automation or decision systems
• Experience with ML/LLM integration and prompt engineering
• Experience with observability tools, metrics, and monitoring systems at scale
• Experience with cloud platforms (GCP/AWS)
• A strong passion for Discord and making online communities safer

Candidates must reside in or be willing to relocate to the San Francisco Bay Area (Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, and Sonoma counties). Relocation assistance may be available.

The US base salary range for this full-time position is $196,000 to $220,500 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits.

About the team/role

We’re hiring an ML Engineer (P2) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data & ML organization within Engineering.

Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily.

This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk.

This role sits in the central Data & ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.

We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results.

What you’ll do

• Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services.
• Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together.
• Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end.
• Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion.
• Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect.
• Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first.

What you bring

• A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience
• 4+ years building software professionally, with at least 2 of those building ML systems that run in production
• Strong Python fluency; you write clean, testable, well-structured code with solid OOP instincts. Not scripts; software
• Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation
• You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped code that other engineers depend on
• Comfortable with distributed systems concepts: queues, async processing, caching, horizontal scaling
• Experience with NLP tasks in production: classification, extraction, entity resolution, summarization
• Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal
• You can evaluate tradeoffs: fine-tune vs. prompt, hosted vs. self-deployed, classical ML vs. LLM, rule vs. model
• Strong communication skills; you explain technical decisions clearly to engineers and non-engineers alike, without hiding behind jargon
• You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it
• An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes.

Nice to have

• Experience with MLOps platforms (MLflow, SageMaker, Vertex, or similar)
• Background in document processing, OCR, or information extraction
• Experience with PySpark or large-scale data processing
• Ruby experience (Checkr’s platform runs on Rails)
• Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech)
• Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools

What you’ll get

• A fast-paced, low-bureaucracy environment where shipping matters more than process
• Direct exposure to executive and C-level decision-making
• A seat on the Data & ML team shaping Checkr’s AI strategy
• Learning and development allowance
• Competitive cash and equity compensation and opportunity for advancement
• 100% medical, dental, and vision coverage
• Up to $25K reimbursement for fertility, adoption, and parental planning services
• Flexible PTO policy
• Monthly wellness stipend

What you'll be doing:

Client Partnership & Implementation

• Own and manage an enterprise client portfolio as primary technical advisor
• Deliver seamless implementations from kickoff through go-live, coordinating internal teams and ensuring timely deployment
• Develop deep knowledge of each client's goals and translate them into actionable plans with measurable results
• Engage clients regularly via Zoom, email, phone, and on-site visits to strengthen relationships and ensure alignment

Adoption & Value Realization

• Drive product adoption with measurable improvements in usage metrics and client health scores
• Conduct technical assessments of API implementations and lead quarterly optimization sessions
• Guide clients on product integrations and troubleshooting in collaboration with Implementation Engineer and Support
• Serve as the first technical point of contact for enterprise clients during implementation and post–go-live, owning technical triage and driving resolution with Implementation and Support.
• Educate clients on best practices to maximize value from Symmetry's solutions

Client Health & Retention

• Monitor client health scores, usage metrics, and interaction to identify risks early and implement mitigation plans
• Provide prompt, professional responses to inquiries ensuring fast resolution and strong confidence
• Support renewal readiness and risk mitigation in partnership with Account Manager

Operational Excellence

• Identify process improvements that reduce implementation time and increase team capacity
• Leverage AI tools and automation to streamline reporting, communication, and coordination
• Maintain thorough documentation in CRM and ticketing systems
• Collaborate with Sales, Product, Engineering, and Support to address issues and drive continuous improvement

Experience & Requirements:

Required Background:

• Bachelor's degree or equivalent combination of education and experience
• 5+ years in Client Success, Account Management, or Implementation Management in B2B SaaS
• Proven track record managing enterprise portfolios and guiding retention, adoption, and satisfaction
• Experience project managing complex software implementations from kickoff through go-live
• Technical aptitude to understand API/SDK integrations, conduct assessments, and troubleshoot issues
• Track record leveraging data and metrics to drive client outcomes and operational efficiency
• Experience in payroll, HR tech, or compliance technology strongly preferred

Core Skills & Attributes:

• Proven technical acumen with ability to translate capabilities into business value
• Extraordinary relationship-building skills to advise enterprise stakeholders on technical and business outcomes
• Outstanding communication skills with ability to simplify complex technical concepts
• Proactive, ownership-driven mindset with accountability for results
• Keen analytical and critical thinking with comfort in qualitative and quantitative analysis
• Great organizational and project management skills managing multiple priorities
• Client-first orientation committed to delivering measurable value
• Collaborative team player thriving in cross-functional partnerships
• Demonstrated ability to leverage AI and automation for efficiency
• Continuous learning mindset staying current on product innovations

Technology Experience:

• Comprehension of API/SDK architecture, RESTful APIs, and integration concepts
• Ability to read API documentation and guide clients through technical implementation
• Experience with API testing tools (Postman or similar) for client troubleshooting
• Proficiency with CRM systems (NetSuite, Salesforce, or comparable)
• Experience with customer success platforms (Catalyst, Gainsight, ChurnZero, or similar)
• Working knowledge of project management and ticketing systems (Jira, Asana, Zendesk, or comparable)
• Proficiency with Google Workspace (Slides and Sheets including formulas and data analysis)
• Experience with virtual meeting platforms (Zoom, Microsoft Teams, or similar)
• Experience with generative AI tools (ChatGPT, Gemini, or comparable) and building AI agents
• Project management certification (PMP, CAPM, Google Project Management) is a plus
• API fundamentals certification (Postman Student Expert or equivalent) is a plus

Compensation Details

Our cash compensation amount for this role is targeted at $121,000/yr- $149,000/yr in Scottsdale. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above. 

About this role:

The Corporate Security team at Faire owns the tools and policies that keep our people, data, and systems protected. The team's scope includes endpoint detection, data loss prevention, email security, corporate threat detection, compliance training, and AI governance. As our Staff Engineer focusing on Enterprise AI, you will be Security and IT's technical leader for AI systems, partnering with engineering teams in driving AI initiatives to ensure governance, security, and enablement scale alongside adoption. You'll work across every level of the organization, balancing the drive to "Make it happen fast" with the discipline to move safely.

What you'll do:

• Own and drive Faire's company-wide AI strategy in partnership with engineering, business, and executive stakeholders. This means setting the direction for which tools we adopt, how we govern them, and how we scale adoption.
• Serve as the technical authority on AI platform administration for tools like Anthropic and OpenAI. You'll evaluate, enable, and disable native connectors, plugins, and features based on risk, security posture, and business value.
• Build and maintain decision frameworks (e.g., risk matrices, enablement criteria) that make AI governance repeatable and transparent.
• Design and engineer secure experimentation infrastructure, including sandboxed environments, isolated MCP connectors for testing new features, and scoped OAuth flows, so teams can safely explore new AI capabilities.
• Design and build custom MCP integrations when out-of-the-box options don't exist or don't meet Faire's security requirements (e.g., overly broad permissions, insufficient audit logging).
• Lead AI pilot programs end-to-end, from scoping and stakeholder alignment through rollout, troubleshooting, feedback collection, and iteration.
• Engineer observability and compliance infrastructure, ensuring compliance logs from AI platforms end up in our SIEM.
• Own the operational mechanics of AI adoption tracking. This includes automating usage data pipelines into Snowflake and partnering with data analysts and domain teams (engineering, CX, etc.) who own their respective adoption metrics.
• Partner with Learning & Development and engineering teams to build and deliver AI training, prompt libraries, and enablement resources tailored to different roles and workflows.
• Embed with teams across the company to understand their workflows, identify where AI can accelerate their work, and just as importantly, where it shouldn't be used.
• Communicate AI strategy, risk trade-offs, and recommendations clearly to audiences ranging from junior engineers to C-level executives, including respectfully challenging perspectives when the data warrants it.
• Stay current with the rapidly evolving AI product landscape and proactively assess new capabilities (e.g., new platform features, agent frameworks) for security implications and business value.

What it takes:

• 7+ years of experience in security engineering, platform engineering, infrastructure engineering, or IT engineering.
• Experience building and presenting decision frameworks, risk assessments, or strategy recommendations to senior leadership.
• Ability to operate independently, setting your own strategy, building stakeholder relationships, and executing with minimal oversight.
• Strong communication skills across a wide range of audiences, from mentoring junior engineers to partnering with executives in strategy discussions.
• Proficiency in at least one object-oriented programming language (e.g., Python, Go, Ruby, Java) with the ability to build integrations, tooling, and automations from scratch.
• Hands-on experience administering or deeply integrating with AI/LLM platforms. Not just using them, but managing them at the platform level (e.g., building MCP servers, extending LLM functionality, managing enterprise AI admin consoles).
• Experience designing scalable internal systems and infrastructure. You think about solving categories of problems at the platform level, not building one-off solutions.
• Track record of owning company-wide programs that delivered measurable impact to downstream users or the business.
• Experience with observability or SIEM tooling (e.g., Datadog, Splunk) and building data pipelines for monitoring and compliance.

Nice to have:

• Understanding of LLM internals, including transformer architectures, tokenization, context window management, RAG patterns, prompt engineering techniques, and the security risks inherent to each (e.g., prompt injection, data leakage through context, output manipulation).
• Prior experience in an AI governance, AI enablement, or AI strategy role.
• Experience with observability or SIEM tooling (e.g., Datadog, Splunk) and building data pipelines for monitoring and compliance.
• Familiarity with security and compliance considerations specific to AI tooling, such as data residency, DLP policies for AI-generated content, agent access controls, and OAuth scoping for third-party integrations.
• Experience building internal enablement programs, whether training courses, documentation, or developer advocacy, that drove measurable adoption of new tools or practices.

Salary Range:

San Francisco: the pay range for this role is $204,000 - $280,500 per year. 

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

About the Role:

CoreWeave is seeking a Staff AI Security Engineer to define and operationalize security across our AI ecosystem. This role sits at the intersection of AI/ML, cloud infrastructure, and security engineering. You'll build secure-by-default foundations for AI development, agentic workflows, and enterprise AI adoption, balancing velocity with strong security guarantees. You will also partner closely with enterprise security and IT teams to manage AI-adjacent risks across the corporate environment, including shadow AI, data exfiltration through AI tooling, and SaaS security posture for AI-enabled platforms.

What You'll Do:

• Design and implement security controls across AI/ML infrastructure, including model artifact storage, data lineage and integrity, model signing and provenance, and ML pipeline security (MLOps/MLSecOps)
• Develop AI security policies, standards, and threat models covering model development, training pipelines, data ingestion, inference environments, and agentic systems
• Build security guardrails for agentic workflows: tool access and permissioning, input/output validation, execution boundaries, sandboxing, and auditability of agent actions
• Lead secure adoption of AI tools across engineering, security, operations, and enterprise functions. Evaluate AI vendors, copilots, and integrations. Define policies for data sharing, model usage boundaries, and sensitive data handling in prompts and outputs
• Partner with enterprise security and IT on AI-related risks across the corporate environment, including endpoint posture, corporate identity, and SaaS security for AI-enabled platforms
• Conduct threat modeling and adversarial testing of AI systems, covering prompt injection, data poisoning, model extraction, and backdoored models. Build and maintain a CoreWeave-specific AI threat taxonomy
• Drive cross-team technical decisions and influence architecture across platform engineering, security engineering, ML/AI teams, and product/DevEx to embed security into AI platforms and developer tooling

Who You Are:

Minimum Qualifications

• 10+ years of experience in security engineering (cloud, application, or infrastructure)
• Experience with AI/ML systems: LLMs, training pipelines, inference systems, or MLOps
• Strong, demonstrable experience building and securing large-scale distributed systems
• Strong coding skills in Go, Python, or similar
• Familiarity with Kubernetes, containerized environments, and cloud platforms (AWS, GCP, Azure)
• Understanding of AI-specific threats: prompt injection, data leakage, model misuse, supply chain risks in models and datasets
• Track record of driving cross-team technical initiatives and influencing architecture decisions at the org level

Preferred:

• Experience building or securing LLM-based systems, agent frameworks (LangChain, etc.), or AI-powered internal tools
• Background in adversarial ML or red teaming AI systems
• Experience with secure model deployment pipelines or confidential computing / secure enclaves
• Familiarity with identity and access control systems
• Experience in high-performance or GPU-centric environments
  
  

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $188,000 to $275,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Every day, millions of riders and drivers depend on Lyft — and when something goes wrong, we want to make it right as fast as possible. The Self-Serve Intelligence team within Safety & Customer Care org is building the AI-powered systems that do exactly that: resolving rider and driver suboptimal experiences without agent involvement through intelligent solutions in AI Assist (e.g. AI Agents), automations, and self-serve workflows. We're building backend systems, APIs, and AI-powered products that fundamentally rethink how customers get help — combining robust engineering with GenAI to deliver reliable, scalable self-serve experiences.

As a Senior Software Engineer on the Self-Serve Intelligence team, you will design, build, ship, and maintain backend services and AI-powered products (e.g. various AI Agents) that autonomously resolve customer suboptimal experiences. You'll own the full lifecycle — from system design and prototyping through production deployment and operational excellence — partnering with product, design, data science, and operations to deliver high-quality, reliable solutions at scale.

Responsibilities:

• Design, build, ship, and maintain scalable backend services and GenAI-powered products (e.g. AI Agents) that resolve rider and driver suboptimal experiences autonomously
• Develop robust evaluation frameworks to measure and improve the quality and impact of GenAI-driven customer experiences
• Lead projects from idea to production, owning end-to-end system design, implementation, and operational health
• Write well-crafted, well-tested, readable, maintainable code
• Design and evolve APIs, data models, and service architectures that support rapid product iteration
• Rapidly adopt and integrate emerging AI capabilities to accelerate development velocity across the team
• Incorporate considerations for edge cases, failure modes, and business context into system design
• Participate in code and architecture reviews to ensure quality and distribute knowledge
• Own system reliability — monitor, debug, and improve service health, latency, and availability
• Proactively participate in resolving ongoing incidents
• Unblock, support, and effectively communicate across teams to achieve results
• Share your knowledge by giving brown bags, tech talks, and evangelizing engineering and AI best practices

Experience:

• 5+ years of software engineering industry experience
• Strong backend engineering fundamentals — designing, building, and operating distributed systems and microservices in production
• Hands-on experience with LLMs, agentic workflows, or building AI Agents — we value depth of AI experience and treat it as a strong differentiator
• Track record of building, evaluating, and shipping GenAI or ML-powered products
• Experience defining API schemas and developing backend services in a microservices environment
• When facing an ambiguous problem or outside your expertise, you can quickly learn what you need to dig in, make sense of it, and start working toward a solution
• You're a great communicator who can advocate for your proposals while empathizing with your teammates' goals and priorities
• You have a history of going out of your way to help your fellow engineers be more productive

It would be a huge plus to have:

• Strong proficiency in Python
• Experience with AI agent frameworks, prompt engineering, or model evaluation pipelines

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $185,000 - $222,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

With over half a billion rides and counting, Lyft is solving hard problems at scale, leveraging AI and Machine Learning to better serve our customers. The Artificial Intelligence, Machine Learning, and Operations Research Platforms team (AIMLOR) is seeking a Senior Machine Learning Engineer to focus on building AI Platform components enabling critical AI applications across Lyft. Expertise with GenAI and platform building is a core requirement for this role. In this role, you will contribute to our platform which supports real-time, online, and offline AI and ML model execution, development, and iteration. You will work with a team of highly motivated Machine Learning and Software Engineers on challenging problems, defining solutions to directly impact systems across the entire business.

If you are interested in building an AI Platform at scale, with applications across each facet of the company, we are searching for you.

If you are a creative and critical thinker with experience in AI and machine learning systems, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you.

Responsibilities:

• Contribute to the roadmap and architecture based on technology and business needs
• Write well-crafted, well-tested, readable, maintainable code
• Have a good grasp and ability to explain the various tradeoffs made in decisions
• Participate in code reviews to ensure code quality and distribute knowledge 
• Build features from tech specification to positive execution
• Incorporate considerations for business context and failure modes in your work
• Proactively participate in resolving ongoing incidents 
• Unblock, support, effectively communicate, and obtain buy-in within your team to achieve results
• Share your knowledge by giving brown bags and tech talks

Experience:

• BSc/MSc in Computer Engineering, Computer Science, Machine Learning related field or relevant work experience
• 5+ years of ML engineering experience working in any of these stacks: Python, GO, Java, etc.
• Experience with ML serving/training/deployment infrastructure; familiarity with cloud providers (e.g. AWS, Azure, Google Cloud); familiarity with GenAI ecosystem: LLMs, prompt engineering, MCP, RAG; hands-on experience with LLM fine-tuning techniques and frameworks (e.g. PEFT, LoRA); knowledge on deploying self-hosted LLMs (e.g. Llama, Mistral) for specialized tasks
• Experience with GenAI/LLM infrastructure, agent frameworks (e.g. LangChain, LangGraph); MLOps tooling (MLflow, Airflow)
• Preferred: Experience with AI assisted coding such as Cursor or Claude Code

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $162,800 - $203,500, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Machine Learning is at the heart of Lyft’s products and decision-making. Machine Learning Engineers at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges, from pricing and marketplace frameworks that ensure reliability and competitiveness, to agentic AI platforms that automate analytical workflows, to behavioral detection systems that protect the integrity of our network. We operate at the intersection of applied ML and real business impact, shipping models that directly influence revenue, rider experience, and partner trust.

Lyft Business builds products that help organizations move the people who matter most—employees, customers, patients, and guests—easily and efficiently. Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated tools. These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and community programs.

We're looking for a Machine Learning Engineer to design, build, and deploy ML systems across Lyft Business. This is a high-scope role: you won't be siloed into one problem area. Instead, you'll move across pricing algorithms, fraud and behavior detection, agentic AI systems, and emerging ML applications as the business evolves. You'll write production-quality code, own models end-to-end from prototyping through deployment, and collaborate closely with Data Scientists, Product Managers, and Software Engineers to translate complex business problems into scalable ML solutions.

This role is ideal for someone who is technically versatile, energized by variety, and wants to see their work directly shape a large-scale business.

Responsibilities:

• Develop and deploy ML models across multiple problem domains — including dynamic pricing, marketplace optimization, fraud detection, and anomaly/behavior detection — in production environments serving millions of rides
• Build and iterate on agentic AI systems (e.g., LLM-powered analytical agents) that automate decision-making and reduce operational overhead
• Design and implement feature pipelines, model training workflows, and serving infrastructure using Lyft's ML platform
• Partner with Data Scientists on the Algorithms and Decisions teams to take research prototypes from proof-of-concept to production at scale
• Evaluate ML system performance against business KPIs, run experiments, and drive continuous model improvement
• Identify new opportunities where ML can create leverage across Lyft Business verticals (Healthcare, Lyft Pass, Business Travel) and pitch solutions
• Contribute to team engineering standards — code quality, observability, documentation, and testing practices

Experience:

• Experience with GenAI / LLM ecosystems — prompt engineering, RAG, agent frameworks (e.g., LangChain, LangGraph), or fine-tuning
• Exposure to graph-based ML methods (graph neural networks, knowledge graphs, network analysis)
• Experience with pricing, marketplace, or fraud-related ML problems
• Familiarity with cloud ML services (AWS SageMaker, Bedrock) or internal ML platforms
• Track record of identifying and scoping ML projects independently, not just executing on pre-defined specs

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $176,000-$211,200, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

With over half a billion rides and counting, Lyft is solving hard problems at scale, leveraging AI and Machine Learning to better serve our customers. The Artificial Intelligence, Machine Learning, and Operations Research Platforms team (AIMLOR) is seeking a Senior Machine Learning Engineer to focus on building AI Platform components enabling critical AI applications across Lyft. Expertise with GenAI and platform building is a core requirement for this role. In this role, you will contribute to our platform which supports real-time, online, and offline AI and ML model execution, development, and iteration. You will work with a team of highly motivated Machine Learning and Software Engineers on challenging problems, defining solutions to directly impact systems across the entire business.

If you are interested in building an AI Platform at scale, with applications across each facet of the company, we are searching for you.

If you are a creative and critical thinker with experience in AI and machine learning systems, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you.

Responsibilities:

• Contribute to the roadmap and architecture based on technology and business needs
• Write well-crafted, well-tested, readable, maintainable code
• Have a good grasp and ability to explain the various tradeoffs made in decisions
• Participate in code reviews to ensure code quality and distribute knowledge 
• Build features from tech specification to positive execution
• Incorporate considerations for business context and failure modes in your work
• Proactively participate in resolving ongoing incidents 
• Unblock, support, effectively communicate, and obtain buy-in within your team to achieve results
• Share your knowledge by giving brown bags and tech talks

Experience:

• BSc/MSc in Computer Engineering, Computer Science, Machine Learning related field or relevant work experience
• 5+ years of ML engineering experience working in any of these stacks: Python, GO, Java, etc.
• Experience with ML serving/training/deployment infrastructure; familiarity with cloud providers (e.g. AWS, Azure, Google Cloud); familiarity with GenAI ecosystem: LLMs, prompt engineering, MCP, RAG; hands-on experience with LLM fine-tuning techniques and frameworks (e.g. PEFT, LoRA); knowledge on deploying self-hosted LLMs (e.g. Llama, Mistral) for specialized tasks
• Experience with GenAI/LLM infrastructure, agent frameworks (e.g. LangChain, LangGraph); MLOps tooling (MLflow, Airflow)
• Preferred: Experience with AI assisted coding such as Cursor or Claude Code

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Seattle area is $149,780 - $187,220, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

The Senior Manager, Engineering - AI Agents will lead the development and delivery of Five9’s AI Agent software platform, which provides customers with fully autonomous self-service agents for customer support, sales, and services use cases in the contact center. This is one of the hottest areas of application for AI Agent technology! 

This role is responsible for building and managing a high-performing engineering team that develops scalable, reliable, and production-grade AI-powered systems used in real-time customer conversations. 

You will be responsible for hiring and developing engineers, improving engineering processes, and enabling strong execution across the software development lifecycle. While this is a management role rather than an individual contributor position, the ideal candidate will bring strong technical credibility with engineers and guide architecture discussions, technical decisions, and engineering best practices. 

You will collaborate closely with Product, SRE, Support, and other engineering teams to deliver an enterprise-grade platform while fostering a culture of operational excellence, ownership, and customer empathy. 

This role is hybrid. 3 days a week (M, W, TH) in our San Francisco office is required.

Key Responsibilities 

Team Leadership & Development 

• Hire, onboard, and grow a high-performing engineering teamof 4-8 engineers in the United States 
• Provide coaching, mentorship, and career development for engineers
• Establish clear ownership, accountability, and engineering rigor
• Conduct performance management, feedback, and growth planning
• Foster a culture of collaboration, innovation, and operational excellence
• Mentor senior engineers and emerging technical leaders

Delivery & Execution 

• Lead planning and enable strong execution of engineering initiatives and product roadmap
• Ensure projects are delivered predictably and with high quality
• Focus on customer success, ensuring quality AI agent experiences with low latency and high accuracy
• Break down large initiatives into achievable milestones
• Partner with Product Management to prioritize work and manage trade-offs
• Collaborate with Forward Deployed Engineering (FDE) teams on AI agent implementations
• Drive continuous improvement in development processes and team productivity

Technical Leadership 

• Perform code reviews, guide software architecture  andtechnical decisions  
• Review technical designs and ensure sound engineering practices
• Help teams make pragmatic technology decisions balancing innovation and reliability
• Drive the team to manage customer expectations of uptime and performance by defining and reviewing SLO/SLI's, developing metrics to measuring as such and maintainingfor all the services 
• Ensure systems are scalable, maintainable, and production-ready

Operational Excellence 

• Promote strong service ownership and reliability culture
• Ensure effective on-call practices and incident response processes
• Drive root cause analysis and continuous reliability improvements
• Partner with SRE teams to define SLIs, SLOs, and operational metrics
• Handle customer escalations and issues related to AI Agent quality
• Participate in on-call escalation rotations for provided services

Cross-Functional Collaboration 

• Work closely with Product, UX, SRE, Forward Deployed Engineering (FDE) and Support teams
• Align engineering execution with product strategy and customer needs
• Communicate engineering progress, risks, and technical decisions clearly to stakeholders

AI-Augmented Engineering Practices 

• Use of modern AI-assisted development tools to improve engineering productivity, and represent “best in class” examples of how to effectively use it
• Help the team adopt responsible and effective use of AI in software development workflows
• Evaluate emerging developer AI tools and incorporate them wherebeneficial 
• Promote experimentation and learning around AI-enabled engineering practices

Required Experience 

• 3+ years experience leading or managing software engineering teams 
• 8+ years professional software development experience
• Experience participatingin hiring and helping grow engineering teams 
• 5+ years experience developing enterprise SaaS software 
• Strong background in distributed systems and modern software architectures
• Experience or exposure to GenAI / LLM-based systems
• Experience using or adopting AI-assisted software development tools
• Bachelor’s or advanced degree in Computer Science, Computer Engineering, or similar
• Strong track recordin hiring outstanding software development teams, through rigorous interviewing and vetting processes – either as the hiring manager or as part of interview panels  

Desired Experience 

• 1-2 years experience building AI Agent platforms for CX, including voicebots, chatbots and AI assistants 

Technical Background 

While this role is primarily management-focused, candidates must have sufficient technical depth to guide architecture and mentor engineers. 

Software Engineering 

• Strong background in Java or JVM-based backend systems 
• Experience designing distributed systems or microservices architectures 
• Familiarity with API design (REST/gRPC) and scalable service architectures 

AI / GenAI Platforms 

• Familiarity with LLM-based systems, prompt engineering, AI Agent architectures and design patterns 
• Understanding of common AI platform patterns such as embeddings, RAG pipelines, or model integration 
• Awareness of performance and reliability considerations for AI-backed systems 

Cloud & Infrastructure 

• Experience building SaaS platforms in public cloud environments (GCP preferred but not required) 
• Familiarity with microservices architectures and cloud-native systems 
• Basic understanding of networking concepts such as load balancing, service-to-service communication, and API gateways 

Operational Excellence 

• Experience operating production services in enterprise SaaS environments 
• Familiarity with observability practices (metrics, logging, tracing) 
• Experience participating in or leading incident response and root cause analysis 

Leadership & Collaboration Skills 

• Proven ability to build and develop strong engineering teams 
• Strong mentoring and coaching capabilities 
• Ability to guide engineers through technical design and problem solving 
• Excellent communication skills across engineering and product organizations 
• Ability to align teams around clear priorities and ownership 

What Success Looks Like in This Role 

• Hire a strong engineering team delivering reliable AI platform capabilities 
• Predictable and high-quality delivery of engineering initiatives for our core AI Agent platform 
• Clear engineering ownership and operational accountability 
* Customer obsession resulting in customer satisfaction with AI Agent quality, latency and accuracy 
• Scalable and maintainable platform architecture 
• Engineers effectively leveraging AI-assisted development tools 
• A culture of accountability, collaboration, and technical excellence 

The Product Solutions Architecture (PSA) team acts as a technical multiplier across Datadog. PSAs are domain experts who partner with Field teams on complex customer use cases across pre- and post-sales engagements and scale their impact by producing reusable collateral, including reference architectures, technical guides, and enablement assets. By feeding real-world customer insights back to Datadog Product teams, PSAs help influence product roadmaps while accelerating adoption, usage, and long-term customer success.

Datadog’s LLM Observability product enables organizations to monitor, troubleshoot, and optimize large-scale LLM-powered applications with confidence, while meeting requirements around data privacy, compliance, and cost management. As a Product Solutions Architect, you will partner closely with Datadog customers and the LLM Observability product team to design architectures, implement best practices, and drive adoption of LLM observability across customer environments.

At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Serve as the in-house subject matter expert for Datadog’s LLM Observability product
• Partner with Field teams to provide hands-on technical and architectural guidance to enterprise customers adopting LLM Observability
• Create high-impact technical collateral, including reference architectures, technical guides, cookbooks, and documentation to enable Field teams and the broader customer community
• Build proofs of concept and small-scale deployments to validate solutions and reproduce real-world customer environments
• Act as a trusted advisor to Product Management by delivering actionable feedback informed by real-world field experience

Who You Are: 

• You bring a strong software engineering foundation, with hands-on experience building and operating LLM-powered applications in production environments, and are comfortable diving deep into codebases to understand architectural decisions, tradeoffs, and implementation details
• You are familiar with distributed systems and core observability concepts, such as tracing, metrics, and logging, and can translate those patterns into the emerging LLM observability domain, including LLM spans, evaluations, and prompt templates
• You have experience with languages such as Python and/or JavaScript/TypeScript
• You are comfortable operating in rapidly evolving, ambiguous technical domains
• You build deep context across teams and translate it into reusable, scalable solutions
• You take ownership from problem definition through implementation and measurable outcomes
• You are highly detail-oriented, particularly when working on architectures and customer-facing technical assets
• You bring strong listening and consultative skills and are experienced supporting customers in high-pressure, complex situations
• Able to sit up to 4 hours, traveling to and from client sites 
• Able to travel via auto, train, or air up to 40% of the time 

Bonus Points:

• Successful track record with 5+ years experience working as an Architect or Consultant 
• Experience using Datadog and/or other observability tools in an SRE or DevOps capacity
• Experience with LLM application evaluation and testing frameworks

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth: 

• Best-in-breed onboarding
• Generous global benefits
• Intra-departmental mentor and buddy program for in-house networking
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• An inclusive company culture, able to join our Community Guilds and Inclusion Talks

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

Engineering at Brex

Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders.

What you’ll do

As an Application Security Engineer, you will contribute to finding and responding to security vulnerabilities across the Brex platform. In this role, you will participate in code reviews, design reviews, penetration testing, and vulnerability management. You will contribute to tooling that performs static and dynamic testing of the Brex platform and supports secure developer workflows. Application Security is part of our wider Financial Scale organization, which means you will work closely with Security Operations, GRC, Product Security, Front End Platform, and IT Infrastructure teams.

We're looking for individuals with a solid foundation in penetration testing and a curiosity for finding vulnerabilities in complex systems. You should have experience identifying and documenting vulnerabilities across common vulnerability classes and be able to communicate their risk clearly to engineering and product teams. This role is highly cross-functional — you'll have the opportunity to collaborate with engineering teams across Brex and grow your security expertise in a fast-moving environment.

Brex is pioneering the next wave of AI-driven financial services for dynamic, high-impact companies like Coinbase, Robinhood, and Anthropic. As we integrate AI across our product suite, this role will give you hands-on experience contributing to AI security at Brex. You'll apply emerging AI security best practices to help secure our agentic features, identify attack surfaces introduced by LLM-powered systems, and partner with product and engineering teams to build AI capabilities our customers can trust with their critical financial operations.

Responsibilities

• Identify vulnerabilities across common vulnerability classes (e.g., OWASP Top 10), document findings clearly, and communicate risk to drive remediation efforts
• Participate in penetration testing and design reviews alongside senior engineers, contributing to the identification of vulnerabilities and insecure designs
• Contribute to internal tooling and automation efforts that support SAST and DAST testing of the Brex platform and promote secure development practices
• Collaborate with engineering and product teams to support the design of secure product features
• Actively contribute to a culture of security awareness through knowledge sharing and peer learning

Requirements

• 4+ years of work experience in Application Security or a related role
• Demonstrated ability to find and document vulnerabilities in complex systems, with clear communication of business risk
• Hands-on experience with a subset of secure development activities, such as code review, threat modeling, or penetration testing
• Experience identifying security risks in AI/ML systems — such as prompt injection, model manipulation, or data poisoning — through work experience, personal projects, CTFs, or bug bounties
• Familiarity with agentic workflows and the ability to reason about attack surfaces introduced by LLM-powered features
• Knowledge of Python or scripting languages to automate tasks and build tooling
• Collaborative mindset paired with strong written and verbal communication skills

Bonus points

• Experience with Kotlin, gRPC, GraphQL, Kubernetes
• Previous experience as a software engineer
• Experience with securing distributed systems in AWS and cloud environments
• Experience with web application security reviews
• Contributions to the wider technical community — open source, public research, CTF participation, blogging, CVEs, or presentations
• Experience submitting to bug bounty or responsible disclosure programs
• Published AI security research or contributions to AI security frameworks

Compensation

The expected salary range for this role is $152,000 - $190,000. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

You may be a good fit if you:

• Have significant software engineering experience, particularly with distributed systems
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning systems and infrastructure
• Thrive in environments where technical excellence directly drives both business results and research breakthroughs
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High-performance, large-scale distributed systems
• Implementing and deploying machine learning systems at scale
• Load balancing, request routing, or traffic management systems
• LLM inference optimization, batching, and caching strategies
• Kubernetes and cloud infrastructure (AWS, GCP, Azure)
• Python or Rust

Representative projects:

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators
• Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads
• Building production-grade deployment pipelines for releasing new models to millions of users
• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage
• Contributing to new inference features (e.g., structured sampling, prompt caching)
• Supporting inference for new model architectures
• Analyzing observability data to tune performance based on real-world production workloads
• Managing multi-region deployments and geographic routing for global customers

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

This is Engineering at Lattice

At Lattice, we build software that helps people and organizations thrive. Our engineering team values maintainable systems, strong collaboration, and thoughtful product experiences.

As we expand AI-powered capabilities across our platform, security is a core part of how we design, build, and operate our product. Our Product Security team partners closely with engineering to ensure we ship features that are secure by default and resilient in production.

What You Will Do

You will work closely with product and platform engineering teams to improve the security of Lattice’s applications and services.

Product Security & Engineering Partnership

• Partner with engineers to identify, triage, and remediate security issues in product features and services
• Participate in security reviews and threat modeling for new features and systems
• Perform security-focused code reviews and help identify common vulnerabilities
• Contribute to secure-by-default patterns, libraries, and tooling in our TypeScript-based stack

Security Tooling & Operations

• Help implement and operate security tooling (SAST, DAST, dependency scanning, etc.)
• Support vulnerability management workflows, including internal findings and bug bounty reports
• Assist in investigating security issues and assessing risk and impact

Cloud & Platform Security

• Collaborate with platform and infrastructure teams to improve application and cloud security posture
• Contribute to improving security practices in AWS-based environments

AI/LLM Security

• Assist in identifying and mitigating risks in AI/LLM-powered features, including prompt injection, data leakage, and unsafe output handling
• Apply emerging best practices (OWASP Top 10 for LLM Applications) to real product use cases

Security Enablement

• • Contribute to security guidance, documentation, and training for engineering teams
  • Help improve how security is integrated into the development lifecycle

What You Will Bring to the Table

Experience that is important for you to have at some level:

• 1–3+ years of experience in product security, application security, or software engineering
• Experience writing and maintaining code in JavaScript/TypeScript (or similar languages like Python or Ruby)
• Familiarity with common web and API vulnerabilities (e.g., OWASP Top 10)
• Exposure to security testing tools (SAST, DAST, dependency scanning, etc.)
• Experience working in or with cloud environments (AWS or similar)

Technical Approach

• Ability to identify common security risks and suggest practical mitigations
• Understanding of secure coding practices and basic security controls
• Interest in how security decisions impact real-world product systems

Collaboration & Growth

• Strong communication skills and ability to work closely with engineering teams
• Willingness to ask questions, learn quickly, and take ownership of well-scoped problems
• Ability to contribute to team discussions and share security context effectively

Experience that would be nice to have:

• Experience with modern web architectures (Next.js, NestJS, GraphQL)
• Familiarity with containerization or Kubernetes
• Experience or interest in AI/LLM security, including

Location Requirement:
This role is open to candidates located in British Columbia or Ontario, Canada. At this time, we are only able to hire employees residing in these provinces.

----

The estimated annual cash salary for this role is CAD $93,750 - CAD $125,000. This position is also eligible for incentive stock options, subject to the terms of Lattice’s applicable plans

Benefits: The Company offers the following benefits for this position, subject to applicable eligibility requirements: Medical insurance; Dental insurance; Life, AD&D, and Disability Insurance; Natural Disaster Support Program; Wellness Apps; Paid Parental Leave, Paid Time off inclusive of holidays and sick time; Working Remotely Stipend; One time WFH Office Set-Up Stipend; Retirement Plan; Financial Planning; and Learning & Development Budget.

*Note on Pay Transparency:

Lattice provides an estimate of the compensation for roles that may be hired as required by state regulations. Compensation may vary based on (a) location, as Lattice factors in specific location when benchmarking compensation for most roles; (b) individual candidate skills and qualifications; and (c) individual candidate experience.

Additionally, Lattice leverages current market data to determine compensation, so posted compensation figures are subject to change as new market data becomes available. The salary, other compensation, and benefits information is accurate as of the date of this posting. Lattice reserves the right to modify this information at any time, subject to applicable law.

#LI-remote

WHAT IS BOX? 

Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.

By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.

WHY BOX NEEDS YOU 

The Solutions Engineering Team at Box includes solutions engineers, value engineering, platform solution engineering, enterprise architects, and demo engineering.  As a Solutions Engineer, you are empowered to sell to business and IT leaders in every space and vertical, and take ownership in crafting customer-centric solutions. You will work alongside the account team to define and expand revenue opportunities, and ensure the solution is ready for cross company deployments. You also act as a critical liaison between Sales and Product; sharing customer feedback with the Product Management, Operations and Engineering functions at Box. Our highest performers have a natural curiosity, develop deep knowledge of the Box platform, have a strength in delivering value and story based demonstrations, and are capable of leading presentations related to the Box security story, technical architecture and product configuration. 

This role is in the San Francisco office, supporting Enterprise clients to expand our footprint and break into new accounts. If you are curious, passionate about problem-solving and enjoy working with leading-edge technology, we encourage you to apply. 

WHAT YOU'LL DO 

• You research, explore and identify customer business problems; you’ll think like a builder and experiment, iterate, and refine to make every customer experience exceptional

• You own the technical win in your account, and take responsibility for driving the technical deal forward

• Understand product integrations and MCP servers

• You lead demos articulating the value of Box selling the full suite of solutions (BoxAI, Box Web, Mobile, Shield, Relay, Sign, Governance, Canvas, Platform, and more)

• Technical quarterback managing the extended team from a solution standpoint

• Ability to showcase strong business acumen and communicate the value of the Box solution to the customer

• You support field marketing events as a Box SME and presenter

• Travel up to 50% to engage with customers in territory

WHO YOU ARE 

We are an AI-first company. This means you approach your work with a growth mindset and find ways to leverage AI to help make faster, smarter decisions that will 10X your impact at Box.

• 4 year degree or equivalent work experience required

• 5+ years of pre-sales, solutions engineering, or technical consulting experience within SaaS, enterprise software, AI, or cloud platforms

• You’re AI-curious and technically fluent — comfortable discussing LLMs, embeddings, automation, and context and prompt engineering concepts with both technical and business audiences

• Strong presentation skills, with the ability to establish trust across a range of technical and non-technical audiences; passionate about showing, not just telling and designing demos and narratives that make the power of Box AI tangible

• Ability to learn, understand and communicate complex technical concepts and develop skills to create compelling solutions that demonstrate business value 

• Displays follow through, stamina and effort in every step of the sales cycle

• Shows creativity when solving a customer's business problem; you experiment, iterate, and refine to make every customer experience exceptional

• Familiarity with APIs, webhooks, languages, and modern architectures (REST, OAuth, JSON, Python, Javascript etc.) is a plus

• Experience working with low code no code automation such as Nintex, Zaphier, Workado, n8n

Box lives its values, with community and in-person collaboration being a core part of our culture. Boxers are expected to work from their assigned office a minimum of 3 days per week.Your Recruiter will share more about how we work and company culture during the hiring process.

At Box, we believe unique and diverse experiences benefit our culture, our products, our customers, our company, and our world. We aim to recruit a passionate, high-performing workforce that reflects the world we live in. If you are head-over-heels about this role but unsure if you meet all the requirements, we encourage you to apply!

EQUAL OPPORTUNITY

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability, and any other protected ground of discrimination under applicable human rights legislation. Box strives to respect the dignity and ‎‎independence of people with disabilities and is committed to giving them the same ‎‎opportunity to succeed as all other employees. Inclusiveness is core to our culture at Box, and we strive to ensure you get the most from your interview experience.

Box makes reasonable accommodations for applicants with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, please complete this form. Reasonable accommodations may include scheduling adjustments, document dictation and beyond.

Notice to applicants in Los Angeles:  Box, Inc and its related branches will consider for employment, qualified applicants with criminal histories in a manner consistent with the Los Angeles Fair Chair Ordinance.  The Fair Chance Ordinance is provided here. 

Notice to applicants in San Francisco:  Box, Inc and its related branches will consider for employment, qualified applicants with criminal histories in a manner consistent with the San Francisco Fair Chair Ordinance.  The Fair Chance Ordinance is provided here. 

For details on how we protect your information when you apply, please see our Personnel Privacy Notice. If you are a California-resident, please read our California Applicant & Candidate Privacy Notice here.

#LI-HYBRID

We’re building the next generation of travel and expense (T&E) technology using AI and large language models. Our mobile platform blends cutting-edge AI with strong product and UX so travelers get an intelligent companion, not just another booking app. The app should understand needs, anticipate requests, and deliver personalized help through conversational UI and rich, immersive interactions.

You’ll work at the intersection of AI and mobile, helping define how travelers interact with this kind of product on iOS.

What You'll Do: 

• Design and ship innovative UX patterns for conversational interfaces and AI-driven experiences on iOS
• Architect and build Swift-first, production grade features using async/await, structured concurrency and modern iOS APIs
• Lead implementation of context-aware experiences using behavior, preferences, and app state with an emphasis on performance and privacy
• Use metrics, experimentation, and validation (analytics, A/B tests, logging) to iterate on features and improve quality over time
• Turn complex AI/ML capabilities into simple, intuitive mobile interactions
• Use AI tools (e.g., Claude Code / LLM‑based coding assistants) to accelerate development while maintaining a high‑quality, well‑tested codebase
• Collaborate across product, design, and backend in a fast-moving environment

What We're Looking For: 

• 5+ years of professional development experience with strong Swift skills, building high quality iOS apps 
• Deep understanding of Swift and the iOS SDK, including:
• Swift concurrency (async/await, Task, actors), structured concurrency, and modern GCD usage
• SwiftUI for building production UIs; familiarity with UIKit and interoperability is a plus
• Experience with Combine or other reactive patterns, and when to prefer them vs. async/await
• Strong grasp of iOS architecture (e.g., MVVM, Clean Architecture, modularization) suitable for AI‑powered, stateful, and real‑time features
• Solid understanding of JSON, REST, WebSockets, streaming APIs and robust networking layers (error handling, retries, offline support)
• Experience with offline storage, background processing, threading and push notifications
• Curiosity about new interaction models (chat, agents, multimodal UI, etc.)
• Experience using AI development tools (e.g., Claude Code, GitHub Copilot, etc.) as part of your workflow, with good judgment about code quality and security
• Familiarity with LLM‑driven features (prompt design, integrating LLM APIs, handling latency, fallbacks, guardrails) and/or conversational UX
• Track record building consumer‑facing apps; ideally you’ve shipped to the App Store and iterated from user feedback and metrics

• BS in Computer Science or related technical field

Project Overview

This is a cutting-edge initiative to integrate Large Language Models (LLMs) with WorldQuant's proprietary strategy management tools. The project aims to provide AI-assisted insights, automate routine analytical tasks, and enable natural language interactions for Portfolio Managers to more efficiently analyze, maintain, and enhance their trading strategies. This involves building foundational infrastructure, developing robust data pipelines for metrics and features, and creating a secure, user-centric interface with a strong focus on protecting intellectual property.

The Role:

We are seeking a talented and motivated Full-Time Software Engineer to play a key role in the development and enhancement of this AI-driven platform. You will be responsible for designing, building, and maintaining critical components of the system, primarily focusing on the Model Context Protocol (MCP) servers that form the backbone of the platform. This includes enhancing existing MCPs, such as our proprietary command-line interface for strategy data, developing new MCPs for internal data system integration, strategy analysis, and performance metrics, and contributing to the secure and effective use of LLMs.

Key Responsibilities

• Design, develop, test, and deploy robust and scalable MCP servers and related tools in Python.
• Enhance existing MCPs, including our internal data access tools, to ensure reliable access to strategy metadata, historical PnL data, and PnL attribution by risk factors.
• Develop new MCPs, including but not limited to MCPs for direct data system integration, MCPs for strategy analytics, and MCPs for performance reporting.
• Design and implement secure prompt engineering techniques to interact with LLMs, ensuring effective information retrieval while minimizing the risk of intellectual property leakage.
• Collaborate with project managers, technical leads, and other developers to define technical specifications and integrate various data sources.
• Work on API development and integration, ensuring seamless data flow between different systems (internal knowledge management systems, simulation systems, data warehouses).
• Implement data parsing and manipulation logic for various formats (JSON, CSV).
• Contribute to the design and implementation of data pipelines for strategy metrics and features.
• Address and resolve bugs and issues in existing MCP tools and infrastructure.
• Participate in code reviews, and contribute to unit and integration testing efforts.
• Create and maintain technical documentation for developed components.
• Stay updated with emerging technologies and methodologies in AI, LLMs, prompt engineering, and financial technology.

What You’ll Bring:

Core Skills:

• Strong proficiency in Python programming.
• Proven experience in API development, integration, and consumption.
• Solid understanding of database interaction, data access patterns, and data modeling.
• Experience with data parsing and manipulation libraries and techniques (e.g., pandas, JSON, CSV).
• Understanding of or experience with prompt engineering principles for Large Language Models
• Awareness of data security and intellectual property protection considerations in the context of AI and LLM applications

Domain-Specific Knowledge (Highly Beneficial):

• Familiarity with financial data (e.g., PnL, Sharpe ratio, alpha, risk factors, AUM).
• Experience with quantitative trading systems, market data systems, or strategy management platforms (familiarity with internal data systems or command-line data tools is a significant plus).

Technical Environment Familiarity (Helpful):

• Experience working in a Linux environment.
• Understanding of Command Line Interface (CLI) tools and development.
• Familiarity with version control systems (e.g., Git).

Soft Skills

• Excellent problem-solving and analytical skills.
• Strong collaboration and communication skills, with the ability to work effectively in a team.
• High attention to detail and commitment to delivering high-quality software.
• Ability to learn quickly and adapt to new technologies and complex systems.

Education

• Bachelor's or Master's degree in Computer Science, Engineering

About the role:

Do you get excited about creating high-impact AI applications that employees rely on every day?

Samsara’s Integrations, Data Engineering, and AI (IDEA) team is a key part of Samsara’s Business Technology organization, driving innovation through data and AI. Our mission is to empower and enable everyone at Samsara with AI-powered tools and capabilities so they can get their work done more effectively. We bridge technology, innovation, and business needs to infuse AI-powered solutions into our organization.

We are looking for a hands-on AI Engineer to build, ship, and scale Samsara’s AI applications and capabilities end-to-end. As an AI Engineer IV, you’ll own the full lifecycle of AI-powered systems — from initial design and prototyping to production deployment, monitoring, and iteration. You will collaborate with architects, engineers, and cross-functional stakeholders across Samsara to brainstorm, design, rapidly prototype, and productionalize AI-driven features and capabilities that integrate deeply into real business workflows.

This role is open to candidates residing in Canada.

You should apply if:

• You want to impact the industries that run our world: Your efforts will result in real-world impact—helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly, ensure workers return home safely.
• You are the architect of your own career: If you put in the work, this role won’t be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development, countless opportunities to experiment, and mastery of your craft in a hyper-growth environment.
• You are energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas for our customers.
• You want to be with the best: At Samsara, we win together, celebrate together, and support each other. You will be surrounded by a high-caliber team that will encourage you to do your best.

In this role, you will:

• Build, ship, and own end-to-end AI-powered applications, from ideation and prototyping through production deployment, monitoring, and ongoing iteration.
• Architect and scale company-wide GenAI capabilities and domain-specific business applications that are embedded into real operational workflows.
• Design and integrate AI solutions with internal systems and third-party platforms, including SaaS tools, APIs, data platforms, and enterprise systems.
• Develop and launch AI-powered solutions to improve efficiency and personalization across business functions such as Customer Support, People/HR, Legal, and Finance.
• Collaborate closely with top-tier engineers, data scientists, and business users in a fast-moving environment.
• Apply and scale software engineering excellence, including reliability, observability, and maintainability of production AI systems.

Minimum Requirements for the role:

• Bachelor’s or Master’s degree in Computer Science or related Engineering discipline.
• 5+ years of industry experience in software engineering or AI product development, including
• 3+ years of experience owning the development, testing, deployment, monitoring, and support of web applications and backend APIs.
• 1+ year of experience building and deploying production-ready Generative AI solutions, with responsibility for reliability and performance.
• Strong expertise in Python and AI/ML fundamentals, particularly Generative AI solutions (e.g., LLMs, vector databases, prompt engineering, embeddings, RAG systems).
• Experience with AI agent development frameworks and tooling (e.g., LangChain, LangGraph, CrewAI, LlamaIndex, OpenAI SDK, MCP, A2A).

An ideal candidate is:

• Experienced in 0→1 MVP development, iterative prototyping, and evolving prototypes into production-grade systems.
• Passionate about staying up to date with industry news, AI development trends, tools, and best practices.
• Knowledgeable about SDLC best practices and actively experiments with and adopts modern AI development tools and patterns.
• An excellent communicator with the ability to work effectively in cross-functional settings and translate business needs into technical solutions.

About the role:

Do you get excited about creating high-impact AI applications that employees rely on every day?

Samsara’s Integrations, Data Engineering, and AI (IDEA) team is a key part of Samsara’s Business Technology organization, driving innovation through data and AI. Our mission is to empower and enable everyone at Samsara with AI-powered tools and capabilities so they can get their work done more effectively. We bridge technology, innovation, and business needs to infuse AI-powered solutions into our organization.

We are looking for a hands-on AI Engineer to build, ship, and scale Samsara’s AI applications and capabilities end-to-end. As an AI Engineer IV, you’ll own the full lifecycle of AI-powered systems — from initial design and prototyping to production deployment, monitoring, and iteration. You will collaborate with architects, engineers, and cross-functional stakeholders across Samsara to brainstorm, design, rapidly prototype, and productionalize AI-driven features and capabilities that integrate deeply into real business workflows.

This role is open to candidates residing in the US.

You should apply if:

• You want to impact the industries that run our world: Your efforts will result in real-world impact—helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly, ensure workers return home safely.
• You are the architect of your own career: If you put in the work, this role won’t be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development, countless opportunities to experiment, and mastery of your craft in a hyper-growth environment.
• You are energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas for our customers.
• You want to be with the best: At Samsara, we win together, celebrate together, and support each other. You will be surrounded by a high-caliber team that will encourage you to do your best.

In this role, you will:

• Build, ship, and own end-to-end AI-powered applications, from ideation and prototyping through production deployment, monitoring, and ongoing iteration.
• Architect and scale company-wide GenAI capabilities and domain-specific business applications that are embedded into real operational workflows.
• Design and integrate AI solutions with internal systems and third-party platforms, including SaaS tools, APIs, data platforms, and enterprise systems.
• Develop and launch AI-powered solutions to improve efficiency and personalization across business functions such as Customer Support, People/HR, Legal, and Finance.
• Collaborate closely with top-tier engineers, data scientists, and business users in a fast-moving environment.
• Apply and scale software engineering excellence, including reliability, observability, and maintainability of production AI systems.

Minimum Requirements for the role:

• Bachelor’s or Master’s degree in Computer Science or related Engineering discipline.
• 5+ years of industry experience in software engineering or AI product development, including
• 3+ years of experience owning the development, testing, deployment, monitoring, and support of web applications and backend APIs.
• 1+ year of experience building and deploying production-ready Generative AI solutions, with responsibility for reliability and performance.
• Strong expertise in Python and AI/ML fundamentals, particularly Generative AI solutions (e.g., LLMs, vector databases, prompt engineering, embeddings, RAG systems).
• Experience with AI agent development frameworks and tooling (e.g., LangChain, LangGraph, CrewAI, LlamaIndex, OpenAI SDK, MCP, A2A).

An ideal candidate is:

• Experienced in 0→1 MVP development, iterative prototyping, and evolving prototypes into production-grade systems.
• Passionate about staying up to date with industry news, AI development trends, tools, and best practices.
• Knowledgeable about SDLC best practices and actively experiments with and adopts modern AI development tools and patterns.
• An excellent communicator with the ability to work effectively in cross-functional settings and translate business needs into technical solutions.

Please note: This position is not eligible for immigration sponsorship. 

Company Overview

We are the better way to work in finance. As private equity’s value creation partner, we sit at the heart of PE—where sponsors and CFOs meet. Through financial consulting rooted in data, technology, and AI, we help clients drive value—supporting the office of the CFO to drive end-to-end value creation.

If you crave challenging work and are looking to grow, come solve complex issues alongside 1,600+ finance & technology experts in a supportive, collaborative environment.

Backed by premier private equity firms and headquartered in New York with 11 offices around the globe, we are a high-growth, entrepreneurial firm looking for people who want to be part of building something great. Come make your mark.

Principal Quality Automation Architect

The Principal Quality Automation Architect designs and scales Accordion's automated quality framework across products, services, and AI-enabled workflows. This impactful role will be critical in owning the architecture behind automated validation, test harnesses, evaluation pipelines, test data strategy, and cross-pod tooling standards.

This position will ideally be based in one of our US offices and is a hybrid role with the flexibility to work remotely 2 days per week (also eligible for remote candidates).

This position is not eligible for immigration sponsorship.

What You’ll Do:

Automation Architecture

• Design and implement the core automation framework for functional, API, integration, regression, and workflow testing
• Define standards for reusable test assets, environment usage, data setup, and evidence capture
• Create scalable approaches to testing complex, multi-system workflows

AI Evaluation Infrastructure

• Build and maintain regression harnesses for AI-enabled features, including prompt and workflow comparisons, scored evaluation runs, and benchmark execution
• Partner with AI quality analysts to support rubric-based testing and measurable release criteria

Test Data & Environment Strategy

• Define approaches for test data, synthetic data, masked data, and scenario generation
• Improve reliability and repeatability of validation environments

Engineering Partnership

• Work closely with software engineers to improve testability, observability, CI/CD integration, and defect prevention
• Help pods move quality checks earlier in the development lifecycle

You Have:

• 7+ years of experience in QA automation, SDET, or quality engineering roles, with a focus on scale, repeatability, and leverage
• Strong experience with test automation frameworks, API testing, CI/CD, and integration validation – you are systems-minded and deeply technical
• Strong scripting or coding skills
• Experience working with data-intensive or workflow-heavy applications
• Exposure to AI or LLM validation tooling or strong interest in building in that space
• Ability to design frameworks, not just individual tests, and comfortability setting standards that others adopt
• Commitment to balancing sophistication with delivery pace
• Bachelor's degree preferred

You Are:

• Excited to be part of a growing team, with a focus on driving future growth
• Passionate about delivering exceptional client service
• Knowledgeable about the relationship between Private Equity sponsors and their portfolio companies, particularly within the office of the CFO
• Willing to get your hands dirty in the details of a project while simultaneously seeing the whole picture
• Comfortable managing projects with multiple complex workstreams while also focused on your single workstream (you are managing and doing)
• A self-starter with a strong work ethic
• Full of entrepreneurial spirit and comfortable in a fluid, flat organization
• Able to effectively communicate complex issues and solutions, and raise issues to senior team members when necessary
• A team player, able to work with team members across all levels
• Someone who enjoys mentoring others and doing meaningful work
• A leader of others; you lead by example

The annual salary for this role ranges from: $150,000 to $190,000 + benefits + bonus.

Actual compensation packages are determined by evaluating a wide array of factors unique to each candidate, including but not limited to geographic location, skill set, years and depth of experience, education, certifications, cost of labor and internal equity.

Accordion is an Equal Opportunity Employer. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills.  We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical​​​ condition, pregnancy, genetic information, gender, sexual orientation, gender identity or ​expression, veteran status, or any other status protected under federal, state, or local law.

Please note that as per Accordion policy, we do not accept unsolicited resumes from third-party recruiters unless such recruiters are engaged to provide candidates for a specified opening and in alignment with our inclusive diversity values. Any employment agency, person or entity that submits an unsolicited resume does so with the understanding that Accordion will have the right to hire that applicant at its discretion without any fee owed to the submitting employment agency, person or entity.

#LI-HA1

What The Role Is

We are seeking a talented Senior Data Engineer to design, build, and maintain our data infrastructure supporting mission-critical energy operations. You'll work at the intersection of renewable energy and data, developing pipelines that process everything from real-time asset performance data to complex trading and risk analytics. This hybrid role offers the opportunity to make a direct impact on clean energy operations while working with a cutting-edge data stack including Snowflake, Dagster, dbt, Modal, and GitLab.

What You'll Be Doing

Analytics Infrastructure & Data Warehouse Management

• Design, deploy, and maintain scalable data infrastructure to support enterprise analytics and reporting needs
• Manage Snowflake instances, including performance tuning, security configuration, and capacity planning for growing data volumes
• Optimize query performance and resource utilization to control costs and improve processing speed

Data Pipeline Development & Orchestration

• Build and orchestrate complex ETL/ELT workflows using Dagster to ensure reliable, automated data processing for asset management and energy trading
• Develop robust data pipelines that handle high-volume, time-sensitive energy market data and asset generation and performance metrics
• Implement workflow automation and dependency management for critical business operations

Data Transformation & Analytics Support

• Develop and maintain dbt models to transform raw data into business-ready analytical datasets and dimensional models
• Create efficient SQL-based transformations for complex energy market calculations and asset performance metrics
• Support advanced analytics initiatives through proper data preparation and feature engineering

Data Quality & Governance

• Implement comprehensive data validation, testing, and monitoring frameworks to ensure accuracy and consistency across all energy and financial data assets
• Establish data lineage tracking and privacy controls to meet regulatory compliance requirements in the energy sector
• Develop alerting and monitoring systems for data pipelines, including error handling, SLA monitoring, and incident response

CI/CD & DevOps

• Lead continuous integration and deployment initiatives for Dagster and dbt pipelines, and Streamlit/Gradio application deployments to Linux servers
• Implement automated testing and deployment automation for data pipelines and analytics applications
• Manage version control and infrastructure as code practices

Cross-functional Collaboration

• Partner with Analytics Engineers, Data Scientists, and business stakeholders to understand requirements and deliver solutions
• Work closely with asset management and trading groups to ensure real-time data availability for market operations and risk calculations
• Collaborate with credit risk teams to develop data models supporting financial analysis and regulatory reporting
• Translate business requirements into technical solutions and communicate data insights to stakeholders

Documentation & Security

• Create and maintain technical documentation, data dictionaries, and onboarding materials for data assets
• Implement role-based access controls, data encryption, and security best practices across the data stack
• Monitor and optimize cloud infrastructure costs, implement resource allocation strategies, and provide cost forecasting

What You'll Bring

• Experience: 4+ years of hands-on data engineering experience in production environments
• Education: Bachelor's degree in Computer Science, Engineering, or a related field
• Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
• Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
• Data Transformation: Proficiency in dbt for data modeling, testing, documentation, and version control of analytical transformations
• Programming: Strong Python and SQL skills for data processing and automation
• CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
• Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
• Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
• Data Integration: Experience with APIs, various data connectors, and formats
• Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
• AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
• Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
• Communication: Ability to work effectively with both technical and business stakeholders
• Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight

What Would Be Nice

• Experience in the energy, utilities, or financial services industries
• Knowledge of energy trading concepts, market data, or asset management
• Familiarity with data governance frameworks and regulatory compliance (SOX, GDPR, CCPA)
• Experience with infrastructure as code (Terraform, CloudFormation)

Clearway will not sponsor non-immigrant visas for this position (H-1B, TN, E-3, etc.).

#LI-Hybrid

What The Role Is

We are seeking a talented Senior Data Engineer to design, build, and maintain our data infrastructure supporting mission-critical energy operations. You'll work at the intersection of renewable energy and data, developing pipelines that process everything from real-time asset performance data to complex trading and risk analytics. This hybrid role offers the opportunity to make a direct impact on clean energy operations while working with a cutting-edge data stack including Snowflake, Dagster, dbt, Modal, and GitLab.

What You'll Be Doing

Analytics Infrastructure & Data Warehouse Management

• Design, deploy, and maintain scalable data infrastructure to support enterprise analytics and reporting needs
• Manage Snowflake instances, including performance tuning, security configuration, and capacity planning for growing data volumes
• Optimize query performance and resource utilization to control costs and improve processing speed

Data Pipeline Development & Orchestration

• Build and orchestrate complex ETL/ELT workflows using Dagster to ensure reliable, automated data processing for asset management and energy trading
• Develop robust data pipelines that handle high-volume, time-sensitive energy market data and asset generation and performance metrics
• Implement workflow automation and dependency management for critical business operations

Data Transformation & Analytics Support

• Develop and maintain dbt models to transform raw data into business-ready analytical datasets and dimensional models
• Create efficient SQL-based transformations for complex energy market calculations and asset performance metrics
• Support advanced analytics initiatives through proper data preparation and feature engineering

Data Quality & Governance

• Implement comprehensive data validation, testing, and monitoring frameworks to ensure accuracy and consistency across all energy and financial data assets
• Establish data lineage tracking and privacy controls to meet regulatory compliance requirements in the energy sector
• Develop alerting and monitoring systems for data pipelines, including error handling, SLA monitoring, and incident response

CI/CD & DevOps

• Lead continuous integration and deployment initiatives for Dagster and dbt pipelines, and Streamlit/Gradio application deployments to Linux servers
• Implement automated testing and deployment automation for data pipelines and analytics applications
• Manage version control and infrastructure as code practices

Cross-functional Collaboration

• Partner with Analytics Engineers, Data Scientists, and business stakeholders to understand requirements and deliver solutions
• Work closely with asset management and trading groups to ensure real-time data availability for market operations and risk calculations
• Collaborate with credit risk teams to develop data models supporting financial analysis and regulatory reporting
• Translate business requirements into technical solutions and communicate data insights to stakeholders

Documentation & Security

• Create and maintain technical documentation, data dictionaries, and onboarding materials for data assets
• Implement role-based access controls, data encryption, and security best practices across the data stack
• Monitor and optimize cloud infrastructure costs, implement resource allocation strategies, and provide cost forecasting

What You'll Bring

• Experience: 4+ years of hands-on data engineering experience in production environments
• Education: Bachelor's degree in Computer Science, Engineering, or a related field
• Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
• Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
• Data Transformation: Proficiency in dbt for data modeling, testing, documentation, and version control of analytical transformations
• Programming: Strong Python and SQL skills for data processing and automation
• CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
• Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
• Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
• Data Integration: Experience with APIs, various data connectors, and formats
• Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
• AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
• Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
• Communication: Ability to work effectively with both technical and business stakeholders
• Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight

What Would Be Nice

• Experience in the energy, utilities, or financial services industries
• Knowledge of energy trading concepts, market data, or asset management
• Familiarity with data governance frameworks and regulatory compliance (SOX, GDPR, CCPA)
• Experience with infrastructure as code (Terraform, CloudFormation)

Clearway will not sponsor non-immigrant visas for this position (H-1B, TN, E-3, etc.).

#LI-Hybrid

What The Role Is

We are seeking a talented Senior Data Engineer to design, build, and maintain our data infrastructure supporting mission-critical energy operations. You'll work at the intersection of renewable energy and data, developing pipelines that process everything from real-time asset performance data to complex trading and risk analytics. This hybrid role offers the opportunity to make a direct impact on clean energy operations while working with a cutting-edge data stack including Snowflake, Dagster, dbt, Modal, and GitLab.

What You'll Be Doing

Analytics Infrastructure & Data Warehouse Management

• Design, deploy, and maintain scalable data infrastructure to support enterprise analytics and reporting needs
• Manage Snowflake instances, including performance tuning, security configuration, and capacity planning for growing data volumes
• Optimize query performance and resource utilization to control costs and improve processing speed

Data Pipeline Development & Orchestration

• Build and orchestrate complex ETL/ELT workflows using Dagster to ensure reliable, automated data processing for asset management and energy trading
• Develop robust data pipelines that handle high-volume, time-sensitive energy market data and asset generation and performance metrics
• Implement workflow automation and dependency management for critical business operations

Data Transformation & Analytics Support

• Develop and maintain dbt models to transform raw data into business-ready analytical datasets and dimensional models
• Create efficient SQL-based transformations for complex energy market calculations and asset performance metrics
• Support advanced analytics initiatives through proper data preparation and feature engineering

Data Quality & Governance

• Implement comprehensive data validation, testing, and monitoring frameworks to ensure accuracy and consistency across all energy and financial data assets
• Establish data lineage tracking and privacy controls to meet regulatory compliance requirements in the energy sector
• Develop alerting and monitoring systems for data pipelines, including error handling, SLA monitoring, and incident response

CI/CD & DevOps

• Lead continuous integration and deployment initiatives for Dagster and dbt pipelines, and Streamlit/Gradio application deployments to Linux servers
• Implement automated testing and deployment automation for data pipelines and analytics applications
• Manage version control and infrastructure as code practices

Cross-functional Collaboration

• Partner with Analytics Engineers, Data Scientists, and business stakeholders to understand requirements and deliver solutions
• Work closely with asset management and trading groups to ensure real-time data availability for market operations and risk calculations
• Collaborate with credit risk teams to develop data models supporting financial analysis and regulatory reporting
• Translate business requirements into technical solutions and communicate data insights to stakeholders

Documentation & Security

• Create and maintain technical documentation, data dictionaries, and onboarding materials for data assets
• Implement role-based access controls, data encryption, and security best practices across the data stack
• Monitor and optimize cloud infrastructure costs, implement resource allocation strategies, and provide cost forecasting

What You'll Bring

• Experience: 4+ years of hands-on data engineering experience in production environments
• Education: Bachelor's degree in Computer Science, Engineering, or a related field
• Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
• Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
• Data Transformation: Proficiency in dbt for data modeling, testing, documentation, and version control of analytical transformations
• Programming: Strong Python and SQL skills for data processing and automation
• CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
• Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
• Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
• Data Integration: Experience with APIs, various data connectors, and formats
• Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
• AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
• Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
• Communication: Ability to work effectively with both technical and business stakeholders
• Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight

What Would Be Nice

• Experience in the energy, utilities, or financial services industries
• Knowledge of energy trading concepts, market data, or asset management
• Familiarity with data governance frameworks and regulatory compliance (SOX, GDPR, CCPA)
• Experience with infrastructure as code (Terraform, CloudFormation)

Clearway will not sponsor non-immigrant visas for this position (H-1B, TN, E-3, etc.).

#LI-Hybrid

What We're Looking For

We are looking for a Senior Application Security Engineer to help secure our AI, machine learning, and inference platforms. This is a hands on technical role focused on building security into modern AI infrastructure, inference systems, and developer platforms.

You will work closely with platform engineers, ML engineers, and infrastructure teams to identify risks, design secure architectures, and build security tooling that enables engineers to move quickly and safely.

This role is execution focused. You will drive technical implementation, perform deep security reviews, and help build out our application security capabilities alongside the CISO and engineering leadership.

This role can be based out of one of our office hubs (San Francisco or Seattle) with in-office requirements of at least 2 days per week, plus occasional team/company offsites. We are not able to offer visa sponsorship for this position at this time.

What You’ll Do

Secure AI and Machine Learning Systems

• Perform threat modeling across AI platforms, inference services, and ML pipelines
• Identify risks such as prompt injection, model extraction, adversarial inputs, and data leakage
• Review model serving architectures and inference pipelines
• Partner with ML engineers to secure training, fine tuning, and deployment workflows
• Help design isolation and security controls for multi tenant AI workloads

Application Security Engineering

• Perform architecture and design security reviews
• Conduct targeted code reviews for high risk components
• Identify security gaps in APIs, micro-services, and distributed systems
• Build secure patterns for authentication, authorization, and service to service communication
• Help engineering teams implement secure defaults and guardrails

Inference Platform Security

• Secure customer facing inference APIs and services
• Protect against abuse, model extraction, and adversarial behavior
• Design rate limiting, isolation, and workload protection controls
• Build monitoring and detection for anomalous inference behavior

AI Supply Chain and Model Security

• Evaluate open source models and dependencies
• Secure model artifacts and distribution pipelines
• Implement integrity validation and provenance controls
• Help secure container images and runtime environments

Security Automation and Tooling

• Build security automation for AI and application pipelines
• Integrate security scanning into CI/CD workflows
• Develop tooling to help engineers detect and fix issues early
• Improve developer experience with security guardrails

What You'll Need

Required Experience

• Strong background in application security engineering
• Experience performing threat modeling and architecture reviews
• Experience securing APIs and distributed systems
• Experience working in cloud environments such as AWS, GCP, or Azure
• Experience with containers and Kubernetes
• Strong scripting or programming skills such as Python, Go, or similar
• Experience working closely with engineering teams to implement security improvements

AI and Machine Learning Experience

• Experience securing ML pipelines, inference systems, or data platforms
• Familiarity with risks such as prompt injection, model extraction, and adversarial inputs
• Experience reviewing model serving architectures
• Understanding of training data security and data leakage risks

It's a Strong Plus If You Have

• Red team or offensive security experience
• Experience crafting payloads and evaluating CVEs for exploitability in diverse environments
• Experience with GPU infrastructure or high performance computing
• Experience with Hugging Face, PyTorch, TensorFlow, or similar frameworks
• Experience with LLM systems, RAG pipelines, or agent frameworks
• Experience building security automation pipelines
• Experience securing multi tenant infrastructure

What Success Looks Like

• Security is embedded into AI platform architecture early
• Engineering teams ship quickly with secure defaults
• Inference platforms are resilient against abuse and extraction
• Model pipelines are secure and auditable
• Security tooling scales with engineering growth

Why This Role Matters

This role sits at the intersection of application security, AI infrastructure, and developer platforms. You will help define how we secure modern AI systems while enabling engineers to move quickly and safely.

You will work closely with engineering leadership and the CISO to build practical, scalable security capabilities that support rapid innovation in AI and machine learning.

Compensation