Team Description:

As a Software Engineer on the Agentic AI Team, you'll work directly with the engineers and scientists who use the platform every day. You won't be handed a spec doc from a product manager three layers removed from the problem.

Some days you're heads-down writing harness code, chasing a flaky tool call or shaving minutes off a worker boot. On another day, you might spend a morning pairing with a robotics engineer to figure out why their agent keeps timing out, then spend the afternoon writing a new skill that gives every agent in the company access to a system that didn't exist yesterday. The constant is that you're close to the user and shipping solutions quickly. 

Job Description and Responsibilities:

This role builds the systems that automate digital work at Neuralink. Every day, scientists, engineers, and operators use our agents to perform complex data investigation, query telemetry, debug code, investigate incidents, and stitch data across implants, robots, and clinical trials. 

The platform you'll build is what makes that possible - and what determines how fast the rest of the company can move.

You’ll own a stack that spans dispatchers, persistent worker pools, identity and LLM proxies, session memory, and dozens of skills that connect agents to our internal systems. As more of the company adopts agents, the surface area grows fast - and you'll be the one owning the roadmap and deciding on the highest value projects. You’ll be expected to find new problems at the company to solve.

We're moving fast - model capabilities advance every month, and we're racing to keep our capabilities at the frontier. 60-hour weeks are not uncommon, and there are stretches where the pace is high. This isn't for everyone.

Required Qualifications: 

• A bachelor's degree in Computer Science or equivalent demonstrated work experience
• You've built software that real people use, and you've felt the difference between a demo and a production system
• You can talk to users, understand their workflow, and translate that into a tool an agent can drive reliably. This role is deeply collaborative
• You take ownership: when an agent run breaks in production, you debug it
• You're comfortable with ambiguity:  the patterns for building agentic systems are still being figured out, and a lot of what you build will be the first version of its kind here

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

As Commercial Counsel primarily supporting Stripe’s Stablecoin & Crypto team, you will lead complex commercial and contractual negotiations, advise on product launches, and help scale Stripe’s legal and operational infrastructure. You will work closely with Sales, Product Legal, Partnerships, Risk, Tax, Operations, and external counsel to enable safe, compliant, and commercially sound growth of Stripe’s crypto, stablecoin, and wallet offerings.

We are committed to building an inclusive work environment where all Stripes feel welcomed as their authentic selves—inclusive of all genders, sexual orientations, ethnicities, races, education, ages, and other personal characteristics. We work on broadening our internal diversity because we want to be culturally equipped to build products and solutions for our diverse user base.

What you’ll do

Responsibilities

• Support the Sales team, with particular focus on Stripe’s Stablecoin & Crypto team, leading negotiations and advising on a wide range of commercial and legal issues
• Work closely with Product Legal to bring new products and features to market, including drafting service terms and providing contractual, commercial, and user-experience advice for launches
• Be an integral part of the build out of Stripe’s commercial legal infrastructure to enable scalable, efficient operations in line with Stripe’s growth. This includes advising on the contractual integration of services provided by Stripe’s cryptocurrency, stablecoin, and wallet infrastructure acquisitions (including Bridge and Privy), drafting and maintaining agreements and playbooks, and leading or contributing to projects that improve internal processes and operational efficiencies, including by leveraging AI tooling
• Partner with teams across Stripe, including Sales, Partnerships, Operations, Product, Tax, and Risk groups, to align legal solutions with business priorities
• Serve as a proactive and strategic business partner to senior managers and act as a trusted advisor on commercial strategy and risk
• Develop and deliver training, playbooks, and other resources to help the Sales team sell our products confidently and compliantly
• Act as a point of escalation for outside counsel and manage outside counsel relationships when matters require external expertise

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

• Intellectually curious and a creative problem solver: you relish learning new products and finding creative solutions to complex and evolving legal and commercial problems
• Exceptional drafting skills, with experience in creating services terms or SaaS agreements in addition to deal negotiation drafting
• A self-starter with a bias for action: you are proactive and take ownership to move matters forward
• A clear and concise communicator (written and verbal): you can distill key risks and explain nuanced issues in plain, simple terms for commercial and technical stakeholders
• A natural collaborator: you can guide cross-functional teams toward practical, win-win outcomes
• Familiar with the legal issues and commercial risks arising in connection with hosted data and technology services
• Passionate about technology and able to understand complex business models and product use cases quickly
• A team player willing to do what it takes to contribute to collective success
• Qualified with good standing membership in at least one US state bar with a JD or foreign equivalent
• 6+ years of relevant experience in-house or in a technology practice group at a top law firm

Preferred qualifications

• Direct experience advising on, drafting service terms for, and negotiating transactions involving cryptocurrency, stablecoin, or traditional money-movement products, particularly on a global basis
• Experience in payments or fintech and familiarity with related regulatory frameworks
• Working knowledge of LLM tools and experience in improving legal processes with AI technology
• Prior in-house experience advising high-growth technology companies
• Working knowledge of data protection and privacy regimes such as CCPA, GDPR, and HIPAA

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

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 Accounting Team is responsible for understanding and telling Stripe’s financial story and shaping our collective understanding of how Stripe is performing as a business. Our primary focus is maintaining a well-controlled environment that effectively supports corporate governance, financial reporting and disclosure requirements. We advise and enable Stripe to grow – supporting all products at Stripe, consulting on accounting implications and supporting teams’ ability to make informed strategic decisions. We operate in a fast-paced environment and collaborate significantly with cross-functional and international teams.

What you’ll do

Our Accounting team is seeking an innovative embedded technical resource to help us scale for the future, in a fast-paced environment that’s growing rapidly. You’ll leverage your accounting, artificial intelligence (AI), and technical expertise to build intelligent solutions that enhance Stripe’s accounting capabilities. You’ll develop AI-powered tools and models, autonomous agents, and data products that automate complex workflows and provide actionable insights within a framework of financial reporting control. Working at the intersection of artificial intelligence, accounting, and product development, you'll collaborate with engineering and finance teams to deploy cutting-edge AI applications that scale with Stripe's growth and support our mission of increasing the GDP of the internet. 

Responsibilities

• Apply your accounting and finance domain expertise to identify, explore, and prototype high-impact opportunities where AI can solve complex accounting challenges 
• Build using AI-powered tools and artificial intelligence solutions to automate accounting workflows, enhance controllership, and scale our operations
• Design and deploy autonomous AI agents using frameworks that can reason, plan, and execute complex multi-step problems and enhance decision-making processes through understanding risk and maintaining effective financial reporting controls.
• Ship and maintain interactive data products and self-service analytics tools using SQL and Python frameworks (data visualization and dashboards, low-code application building frameworks) that provide real-time insights and actionable intelligence
• Work across Accounting and partner teams to uncover efficiency opportunities, including AI use cases, document requirements, and translate complex financial workflows into well controlled technical solutions
• Partner with Accounting and SOX teams to assess and develop an effective internal controls approach to AI-based accounting solutions

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

• 10+ years of relevant experience, with at least 4+ years of experience in accounting related or adjacent roles (e.g. finance systems, process improvement) 
• B.A. or B.S degree in Accounting, Finance, Finance Systems or Information Systems
• Fluency in U.S. GAAP
• Advanced proficiency in SQL, including writing complex queries, using CTEs (common table expressions), window functions, and joins for data analysis and pipeline logic
• Familiarity with modern data tools (Python, Databricks, ETL/ELT processes, data pipelines) and coding with AI assistance
• Comprehensive understanding of AI fundamentals and experience with emerging AI/LLM tools, conversational interfaces, prompt engineering, or automation platforms and RPA
• Solution and results-oriented, with a focus on delivering impact in a fast-paced environment 
• Excellent communication and organizational skills, both written and verbal, with the ability to convey financial concepts to both financial and non-financial audiences
• Experience with internal controls (design, implementation and execution)

Preferred qualifications

• Familiarity with the payments industry and fintech landscape

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.

The Team

Our engineering team builds systems that interact with some of the most complex software  ever deployed in production. The team is composed of industry veterans that have built deep learning infrastructure, autonomous drones, ridesharing marketplaces, ad tech and much more. 

We are looking for a client-obsessed AI Solutions Engineer with entrepreneurial tendencies to join the good fight and help build out our Solutions Engineering org. You’ll be the trusted technical advisors for our customers, driving business value, offering advice, and growing accounts. You’ll accomplish this by leading customers to solutions oftentimes by teaching the product to new users or consulting on best practices. You must be ready for technical discussions with data scientists and engineers, then demonstrate the value of Arize in business discussions with directors and executives. The goal is to enable our customers to become successful and enthusiastic about Arize. While we are a remote-first company, the nature of this role requires candidates to be based in the San Francisco Bay area/PST time zone.

What You’ll Do

• Work closely with some of the most sophisticated ML / GenAI teams in the world.
  • You will act as a trusted advisor to our customers, while also building relationships with technical and business stakeholders.
• Advise on GenAI and ML best practices 
• Give ML and LLM product demos to technical and business stakeholders
• Run strategic business reviews for customers in partnership with our sales team
• Interface with our pre-sales engineering team to gather client goals and KPI’s.
• Partner with our product and engineering teams to help drive the product roadmap
• Spearhead new opportunities within existing accounts to help drive expansions.

What We’re Looking For

Note: Even if you do not check every single box, we still encourage you to apply! 

• Previous experience working as a Data Scientist, Machine Learning Engineer, or as an Engineer working with ML models or GenAI applications in production.
• Comfortable working in public Cloud environments (AWS, Azure, GCP)
• Knowledge of machine learning frameworks such as TensorFlow, PyTorch or Scikit-learn
• Knowledge of LLM / Agentic frameworks such as Llamaindex, LangGraph, and DSPy
• Understanding of ML/DS concepts, model evaluation strategies and lifecycle (feature generation, model training, model deployment, batch and real time scoring via REST APIs) and engineering considerations
• Understanding of GenAI concepts and application evaluation + development lifecycle  
• Proficiency in a programming language (Python, JS/TS, Java, Go, etc)
• Strong Communication Skills - Ability to simplify complex, technical concepts.
• A quick and self learner - undaunted by technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV.

Bonus Points, But Not Required

• Previous engineering experience in:
  • Data Science
  • MLOps
  • ML Frameworks
  • LLM / Agentic frameworks
• Customer facing experience strongly preferred such as Solutions Architect, Implementation Specialist, Sales Engineer, Customer Success Engineer, Consultant, or Professional Service roles
• Prior experience working with applications deployed with Kubernetes
• Prior experience demoing technical products to both business and technical audiences

The estimated annual salary and variable compensation for this role is between $125,000 - $175,000, plus a competitive equity package. Actual compensation is determined based upon a variety of job related factors that may include: transferable work experience, skill sets, and qualifications. Total compensation also includes a comprehensive benefit package, including: medical, dental, vision, 401(k) plan, unlimited paid time off, generous parental leave plan, and others for mental and wellness support.

While we are a remote-first company, we have opened offices in New York City and the San Francisco Bay Area, as an option for those in those cities who wish to work in-person. For all other employees, there is a WFH monthly stipend to pay for co-working spaces.

Why Hover wants you

Hover's Customer Experience team is at an inflection point. We've built an exceptional foundation — industry-leading response times, strong CSAT, and a BPO (outsourced) model that handles significant volume. But the next two years require a step change: usage and case volumes are growing quickly, and our vision is that AI will grow to handle 50%+ of total volume.  This requires extensive work across systems, knowledge infrastructure, etc.

Your role will be to build and run our ‘agent team’ alongside our human specialist teams, growing its coverage of our cases to that 50%+ goal and thus preserving our human team’s time and attention to focus on the kinds of cases and situations where their deep expertise and attention is needed.  If you've done 0-to-1 work before, have strong opinions about what good AI QA looks like, and care deeply about how knowledge infrastructure feeds intelligent systems — this is your role.

Our Inbound CX team handles thousands of support conversations per week across phone, chat, and email, serving both self-serve (Product Led Growth) and enterprise (Sales Led Growth) customers. The team includes US-based Senior Specialists who handle our most complex and relationship-critical cases and offshore BPO teams that handle the majority of inbound volume.

You will work in close partnership with GTM Systems Engineering, Analytics Engineering, Revenue Enablement, and other cross functional teams to get the assistance you need building out the knobs and levers that help you run this ‘agent team’ day to day and hit our core operational KPIs.

You will contribute by

This is a rare 0–1 opportunity to define how Hover scales customer experience through AI. You’ll own the full automation roadmap and build systems that directly impact how thousands of customers get help every week.

Some of your core responsibilities include:

• Own the team’s customer-facing agentic bot end-to-end: prompt refinement, escalation logic, routing rules, and ongoing QA calibration
• Design and iterate on human-in-the-loop (HITL) workflows that combine AI-drafted responses with rep review and editing
• Build and maintain Hover's knowledge infrastructure: Guru internal KB, AI-ready support articles, BPO onboarding materials, and documentation standards
• Lead LLM tooling strategy for CX: evaluate tools, configure integrations, and deploy solutions that improve response quality and reduce manual effort
• Define, instrument, and report on all AI performance KPIs: deflection rate, AI CSAT, escalation accuracy, HITL quality score, and KB coverage
• You will act as the connective layer across CX, Engineering, GTM Systems, and Analytics—translating customer needs into scalable AI systems
• Enable BPO and US Specialist teams to work effectively alongside AI tools — training, documentation, and ongoing calibration

Your background includes

• 4+ years of experience in Customer Experience, Support, or Operations
• Day to day building experience with complex AI/LLM powered workflows – prompt and context engineering, data structures, knowledge management, integrations, evaluations, etc.
• Deep experience in the ‘customer support’ world, whether having been a rep yourself, led teams of reps, or partnered extensively for years in a closely supporting manner (engineering, analytics, operations, etc.)
• Experience navigating systems integrations, including comfort working with APIs, webhooks, and the connective tissue between SaaS platforms
• Hands-on QA and Eval experience — you know what good looks like, you can define rubrics, and you can calibrate teams against them
• Data fluency & analytical toolkit — comfortable pulling reports, defining metrics, and using data to make the case for what to build next
• Strong communication skills — you can explain AI systems and their tradeoffs to non-technical stakeholders, and you work fluidly across CX, Engineering, and BizOps

Nice to have

• Direct experience with Intercom, Fin bot, or similar AI-first support platforms
• Experience with Guru or similar internal knowledge tools
• Background in BPO or offshore CX environments
• Familiarity with Salesforce as a data source for CX routing and personalization

Benefits

• Compensation - Competitive salary and meaningful equity in a fast-growing company
• Healthcare - Comprehensive medical, dental, and vision coverage for you and dependents
• Paid Time Off - Unlimited and flexible vacation policy 
• Paid Family Leave - We support work/life balance and offer generous paid parental and new child bonding leave 
• Mandatory Self-Care Days - A day set aside each month to allow employees to recharge 
• Remote Wellbeing Resources - We provide recurring fitness classes, meditation/ mindfulness tools, virtual therapy, and family planning assistance 
• Learning - We encourage continued education and will help cover the cost of management training, conferences, workshops, or certifications

Hybrid roles at Hover

Hover has Hubs in San Francisco and New York City, where we expect that all employees living within a 50-mile radius of our offices will come into their local Hover office at least three times a week to build rapport and foster organic connection. At this time, Hover is not considering fully remote roles.

The US base salary range for this full-time position is $132,000 - $163,000 annually. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all applicable US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

#LI-RH1 #LI-Hybrid

MaintainX is the world's leading Asset and Work Intelligence platform for industrial and frontline environments. We are a modern IoT-enabled cloud-based tool for reliability, safety, and operations on physical equipment and facilities. MaintainX powers operational excellence for 13,000+ businesses including Duracell, Univar Solutions Inc., Titan America, McDonald's, Brenntag, Cintas, Xylem, and Shell.

We recently completed a $150 million Series D funding round, bringing our total funding to $254 million and valuing the company at $2.5 billion.

As the AI GTM Engineer, Growth Marketing, you’ll build and scale the technical foundation behind MaintainX’s acquisition engine. Reporting to the VP, Growth Marketing & Strategy, this is a hands-on, embedded role within the Growth Marketing team, where you will partner directly with marketers to design, build, and launch campaigns powered by automation and AI. You won’t operate as a separate engineering function, you’ll work side-by-side with Customer Marketing, Performance Marketing, SEO & GEO, Lifecycle, and Field Marketing teams to turn campaign ideas into scalable systems and live experiments. This role is critical to building an AI-native, system-driven marketing engine at MaintainX, enabling us to scale acquisition through automation, personalization, and experimentation.

What you'll do:

• Partner with Marketing to Build & Launch Campaigns
• Work directly with Growth marketers to ideate, design, and launch acquisition campaigns
• Translate campaign ideas into scalable systems, automations, and experiments
• Rapidly prototype and iterate on new growth plays (landing pages, outbound, paid, lifecycle)
• Build Systems That Power Campaign Execution
• Develop tooling for programmatic landing pages, ad creative generation, and email workflows
• Enable marketers to launch campaigns faster through reusable systems and automation
• Balance speed (quick tests) with scalability (systems that can be reused)
• Embed AI into Marketing Workflows
• Partner with marketers to identify high-leverage use cases for AI in campaigns
• Build and deploy AI-driven systems for:
• Creative generation and testing
• Personalization across channels
• Outbound messaging and sequencing
• Turn one-off experiments into repeatable campaign infrastructure
• Own Experimentation with Marketing
• Co-design and implement experiments across paid, SEO, lifecycle, and web
• Build tooling to track performance, attribution, and testing outcomes
• Enable faster, data-driven iteration across campaigns

Audience, Segmentation & Personalization

• Partner with marketers to define target audiences and ICP segments
• Build systems to dynamically segment, enrich, and activate audiences across channels
• Enable real-time personalization across the funnel

• Marketing Data & Performance
• Ensure clean data flows from marketing channels into CRM and analytics systems
• Build dashboards and reporting layers for campaign performance and funnel metrics
• Improve visibility into CAC, conversion rates, and pipeline generation 

What you’ll bring:

Technical Skills

• Strong programming ability (Python, JavaScript, or similar)
• Experience working with APIs, automation frameworks, and event-driven systems
• Strong SQL skills and experience with data warehouses (Snowflake, BigQuery, etc.)
• Experience integrating tools like Salesforce, HubSpot, Segment, Webflow, etc.
• Experience deploying AI/LLM workflows into production
• Marketing Systems Experience
• Experience working closely with marketing teams to build and launch campaigns
• Strong understanding of paid media, SEO, lifecycle marketing, and web conversion flows
• Experience building systems that directly support campaign execution and optimization
• Mindset
• Thinks like a marketer, builds like an engineer
• Optimizes for speed, iteration, and measurable impact
• Focused on pipeline and revenue outcomes, not just outputs
• Defaults to automation and scalable systems over manual work

About you:

• 5+ years in growth engineering, marketing engineering, or technical growth roles, with experience building systems that support marketing and acquisition.
• Strong ability to partner with marketers to design and launch campaigns, and translate those into scalable systems and automation.
• Proficiency in SQL and at least one programming language (e.g., Python, JavaScript), with experience building scripts, workflows, or internal tools.
• Experience working with APIs, data pipelines, and integrating marketing tools (e.g., Salesforce, HubSpot, Segment).
• Experience applying AI/LLMs to automate or scale marketing workflows (e.g., content generation, personalization, outbound).
• Solid understanding of paid media, SEO, lifecycle marketing, and experimentation frameworks.
• Strong analytical skills with experience in testing, attribution, and performance measurement.
• Entrepreneurial mindset: thrives in fast-paced, test-and-learn environments with a bias toward speed and execution.
  
  

What’s in it for you:

• Competitive salary and meaningful equity opportunities.
• Healthcare, dental, and vision coverage.
• 401(k) / RRSP enrolment program.
• Take what you need PTO.
• A Work Culture where:
• You’ll work alongside folks across the globe that reflect the MaintainX values, Smart Humble Optimist.
• We believe in meritocracy, where ideas and effort are publicly celebrated.

About us:

Our mission is to make the life of blue-collar workers easier worldwide by creating software that meets their needs and realities. Our product is truly life-changing for 80% of the workforce that doesn’t work behind a desk and needs enterprise-grade software at their fingertips.

MaintainX is committed to creating a diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

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 the Engineering Manager for Cloud Infrastructure, you will lead a highly competent team of senior engineers responsible for the foundational AWS ecosystem that powers Brex. Your primary near-term mission will be driving our high-priority Business Continuity and Disaster Recovery (BCDR) initiative to completion, a company-level priority that involves multi-region failover and infrastructure-wide resilience. You will balance people leadership with deep technical involvement, earning the respect of a senior team while improving operational hygiene and cross-functional coordination across the entire engineering organization.

Where you’ll work

This role will be based in our Seattle office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week: Monday, Wednesday, and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Lead and Grow: Manage and support a senior team of 6 ICs, fostering a culture of high ownership, autonomy, and technical excellence.
• Drive BCDR Strategy: Oversee the execution of the multi-year BCDR initiative, ensuring multi-region infrastructure readiness and automated failover capabilities.
• Technical Stewardship: Maintain and scale Brex’s core cloud infrastructure, including Kubernetes (EKS), RDS/Aurora/Postgres, networking (VPC), and AWS service integrations.
• Cross-Functional Collaboration: Partner with product and engineering teams across Brex to drive adoption of new infrastructure capabilities and ensure successful migrations.
• Operational Excellence: Improve team hygiene by establishing and improving best practices for work tracking (Linear), documentation, and incident response.
• Resource Management: Effectively allocate resources to balance critical project delivery with long-term infrastructure stability.

Requirements

• Leadership Experience: 3+ years of experience directly managing software or infrastructure engineering teams.
• Technical Depth: Deep hands-on experience with cloud infrastructure foundations, specifically AWS, Kubernetes, and Postgres.
• Architectural Insight: Proven ability to design and implement complex infrastructure projects such as multi-region deployments or disaster recovery frameworks.
• Collaborative Mindset: Strong communication skills with the ability to influence technical decisions across multiple autonomous teams.
• Analytical Problem Solving: Ability to dive deep into technical issues while maintaining a high-level strategic view of organizational goals.
• Engineering Background: 5+ years of experience in software or systems engineering.

Bonus points

• Experience with Infrastructure as Code tools, specifically Terraform.
• Background in managing cloud infrastructure cost optimization programs.
• Experience with our tech stack components: Go, Kotlin, Python, or Elixir.
• Passion for leveraging AI and LLM-assisted workflows to increase engineering velocity.

Compensation

The expected salary range for this role is $240,000 - $300,000. 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.

Engineering

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

You will lead an engineering team focused on building the systems and product experiences that power customer activation at Brex,  including onboarding, account setup, verifications, and integrations workflows that help customers realize value quickly. This role requires strategic thinking, operational excellence, technical leadership, and a deep passion for delivering frictionless, AI-enhanced customer journeys. The ideal candidate is an engineering leader with experience scaling user-facing onboarding systems, delivering high-quality product experiences, and partnering deeply across Product, Design, Operations, and GTM teams.

Where you’ll work

This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Take an active role in driving business and product strategies, championing a seamless, intuitive, and efficient onboarding experience.
• Collaborate with cross-functional partners across Product, Design, Operations, and Sales to define priorities and deliver delightful customer activation experiences.
• Leverage AI to reimagine and automate onboarding and implementation workflows, improving speed, personalization, and operational leverage.
• Drive execution of the Onboarding roadmap, ensuring timely, high-quality delivery of systems and features that help customers activate and realize value.
• Lead and manage a team of engineers, including hiring, mentoring, performance management, and establishing strong technical direction.
• Drive continuous improvement in engineering processes, technical architecture, and product quality.
• Foster a culture of innovation, collaboration, accountability, and customer obsession across the team.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 6+ years of software engineering experience with strong technical depth
• 3+ years of experience managing or leading engineers in a high-growth environment.
• Strong technical background and understanding of software development principles.
• Expertise leading full-stack engineering teams delivering end-to-end product experiences.
• Regularly works with cross-functional partners (e.g. Product, Design, Operations, Sales) and excels in driving alignment across stakeholders.
• Data-driven mindset with the ability to evaluate impact, measure funnel performance, and optimize activation metrics.
• Track record building AI-powered product experiences, including LLM-driven automation and personalization.

Bonus points

• Experience with data platforms such as Snowflake, Hex, or similar.
• Experience building systems related to onboarding, implementation, identity, workflow automation, customer lifecycle products, or other customer facing experiences.
• You have started your own technology venture or were an early technical founder/employee. We value entrepreneurial spirit & scrappiness!

Compensation

The expected salary range for this role is $240,000 - $300,000. 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.

Engineering

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

You will lead an engineering team focused on building the systems and product experiences that power customer activation at Brex,  including onboarding, account setup, verifications, and integrations workflows that help customers realize value quickly. This role requires strategic thinking, operational excellence, technical leadership, and a deep passion for delivering frictionless, AI-enhanced customer journeys. The ideal candidate is an engineering leader with experience scaling user-facing onboarding systems, delivering high-quality product experiences, and partnering deeply across Product, Design, Operations, and GTM teams.

Where you’ll work

This role will be based in our New York office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Take an active role in driving business and product strategies, championing a seamless, intuitive, and efficient onboarding experience.
• Collaborate with cross-functional partners across Product, Design, Operations, and Sales to define priorities and deliver delightful customer activation experiences.
• Leverage AI to reimagine and automate onboarding and implementation workflows, improving speed, personalization, and operational leverage.
• Drive execution of the Onboarding roadmap, ensuring timely, high-quality delivery of systems and features that help customers activate and realize value.
• Lead and manage a team of engineers, including hiring, mentoring, performance management, and establishing strong technical direction.
• Drive continuous improvement in engineering processes, technical architecture, and product quality.
• Foster a culture of innovation, collaboration, accountability, and customer obsession across the team.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 6+ years of software engineering experience with strong technical depth
• 3+ years of experience managing or leading engineers in a high-growth environment.
• Strong technical background and understanding of software development principles.
• Expertise leading full-stack engineering teams delivering end-to-end product experiences.
• Regularly works with cross-functional partners (e.g. Product, Design, Operations, Sales) and excels in driving alignment across stakeholders.
• Data-driven mindset with the ability to evaluate impact, measure funnel performance, and optimize activation metrics.
• Track record building AI-powered product experiences, including LLM-driven automation and personalization.

Bonus points

• Experience with data platforms such as Snowflake, Hex, or similar.
• Experience building systems related to onboarding, implementation, identity, workflow automation, customer lifecycle products, or other customer facing experiences.
• You have started your own technology venture or were an early technical founder/employee. We value entrepreneurial spirit & scrappiness!

Compensation

The expected salary range for this role is $240,000 - $300,000. 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.

Engineering

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

You will lead an engineering team focused on building the systems and product experiences that power customer activation at Brex,  including onboarding, account setup, verifications, and integrations workflows that help customers realize value quickly. This role requires strategic thinking, operational excellence, technical leadership, and a deep passion for delivering frictionless, AI-enhanced customer journeys. The ideal candidate is an engineering leader with experience scaling user-facing onboarding systems, delivering high-quality product experiences, and partnering deeply across Product, Design, Operations, and GTM teams.

Where you’ll work

This role will be based in our Seattle office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Take an active role in driving business and product strategies, championing a seamless, intuitive, and efficient onboarding experience.
• Collaborate with cross-functional partners across Product, Design, Operations, and Sales to define priorities and deliver delightful customer activation experiences.
• Leverage AI to reimagine and automate onboarding and implementation workflows, improving speed, personalization, and operational leverage.
• Drive execution of the Onboarding roadmap, ensuring timely, high-quality delivery of systems and features that help customers activate and realize value.
• Lead and manage a team of engineers, including hiring, mentoring, performance management, and establishing strong technical direction.
• Drive continuous improvement in engineering processes, technical architecture, and product quality.
• Foster a culture of innovation, collaboration, accountability, and customer obsession across the team.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 6+ years of software engineering experience with strong technical depth
• 3+ years of experience managing or leading engineers in a high-growth environment.
• Strong technical background and understanding of software development principles.
• Expertise leading full-stack engineering teams delivering end-to-end product experiences.
• Regularly works with cross-functional partners (e.g. Product, Design, Operations, Sales) and excels in driving alignment across stakeholders.
• Data-driven mindset with the ability to evaluate impact, measure funnel performance, and optimize activation metrics.
• Track record building AI-powered product experiences, including LLM-driven automation and personalization.

Bonus points

• Experience with data platforms such as Snowflake, Hex, or similar.
• Experience building systems related to onboarding, implementation, identity, workflow automation, customer lifecycle products, or other customer facing experiences.
• You have started your own technology venture or were an early technical founder/employee. We value entrepreneurial spirit & scrappiness!

Compensation

The expected salary range for this role is $240,000 - $300,000. 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.

Engineering

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

You will lead an engineering team focused on building the systems and product experiences that power customer activation at Brex,  including onboarding, account setup, verifications, and integrations workflows that help customers realize value quickly. This role requires strategic thinking, operational excellence, technical leadership, and a deep passion for delivering frictionless, AI-enhanced customer journeys. The ideal candidate is an engineering leader with experience scaling user-facing onboarding systems, delivering high-quality product experiences, and partnering deeply across Product, Design, Operations, and GTM teams.

Where you’ll work

This role will be based in our Vancouver office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Take an active role in driving business and product strategies, championing a seamless, intuitive, and efficient onboarding experience.
• Collaborate with cross-functional partners across Product, Design, Operations, and Sales to define priorities and deliver delightful customer activation experiences.
• Leverage AI to reimagine and automate onboarding and implementation workflows, improving speed, personalization, and operational leverage.
• Drive execution of the Onboarding roadmap, ensuring timely, high-quality delivery of systems and features that help customers activate and realize value.
• Lead and manage a team of engineers, including hiring, mentoring, performance management, and establishing strong technical direction.
• Drive continuous improvement in engineering processes, technical architecture, and product quality.
• Foster a culture of innovation, collaboration, accountability, and customer obsession across the team.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 6+ years of software engineering experience with strong technical depth
• 3+ years of experience managing or leading engineers in a high-growth environment.
• Strong technical background and understanding of software development principles.
• Expertise leading full-stack engineering teams delivering end-to-end product experiences.
• Regularly works with cross-functional partners (e.g. Product, Design, Operations, Sales) and excels in driving alignment across stakeholders.
• Data-driven mindset with the ability to evaluate impact, measure funnel performance, and optimize activation metrics.
• Track record building AI-powered product experiences, including LLM-driven automation and personalization.

Bonus points

• Experience with data platforms such as Snowflake, Hex, or similar.
• Experience building systems related to onboarding, implementation, identity, workflow automation, customer lifecycle products, or other customer facing experiences.
• You have started your own technology venture or were an early technical founder/employee. We value entrepreneurial spirit & scrappiness!

Compensation

The expected salary range for this role is $240,000 CAD - $300,000 CAD. 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.

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

Data at Brex

The Data organization develops insights, models, and data infrastructure for teams across Brex, including Sales, Marketing, Product, Engineering, and Operations. Our Data Scientists, Analysts, and Engineers work together to make data—and insights derived from data—a core asset across the company.

What you’ll do

As a senior Data Analyst (DA III), you will own the end-to-end analytics lifecycle for one or more business areas at Brex. You’ll go beyond building dashboards—you’ll frame the right questions, design rigorous analyses, apply statistical methods, and translate your findings into clear recommendations for leadership. You will also serve as a technical leader on the Data Analytics team, mentoring more junior analysts and helping define the standards and best practices that elevate the team’s work.

This role sits at the intersection of analytics, analytics engineering, and business strategy. You’ll work in a modern data stack environment and partner closely with Data Scientists, Data Engineers, and senior leaders across the organization.

Where you’ll work

This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Own the analytics lifecycle for assigned business areas: from problem framing and data sourcing through analysis, insight generation, and stakeholder presentation.
• Build and maintain dashboards and self-service reporting tools that enable business teams to independently track performance, identify risks, and make data-driven decisions.
• Write production-quality SQL and Python code to extract, transform, and analyze data at scale.
• Collaborate with Data Engineers and Data Scientists to develop and maintain analytical data models, improve data pipelines, and ensure data quality across the organization.
• Partner with leadership across Sales, Operations, Product, Finance, and other departments to identify high-impact analytical opportunities and deliver actionable recommendations.
• Mentor other data analysts and contribute to the development of team standards, documentation, code review practices, and analytical frameworks.
• Proactively identify gaps in data infrastructure, propose improvements, and contribute to the evolution of the team’s tooling and processes.

Requirements

• 5+ years of experience in data analytics, business intelligence, or a related quantitative role.
• 3+ years of experience partnering directly with Sales, Operations, Product, or equivalent business teams as an embedded analytics partner.
• Advanced SQL proficiency, including CTEs, window functions, performance optimization, and working across complex data models.
• Proficiency in Python for data analysis, automation, and modeling (Pandas, NumPy, scikit-learn, or similar).
• Experience with cloud data warehouses, particularly Snowflake (BigQuery and Databricks also valued).
• Hands-on experience with BI and data visualization tools (Looker, Tableau, Hex, or similar).
• Strong stakeholder management skills—proven ability to present complex technical findings to non-technical audiences.
• Experience with generative AI and LLM-based tools (Claude Code, Cursor, GitHub Copilot) to perform and accelerate analyses, automated reporting, and build self-service data tools.

Bonus points

• Demonstrated experience applying statistical methods to business problems (e.g., regression, classification, A/B testing).
• Experience with dbt for data modeling and transformation.
• Experience building and maintaining data pipelines using orchestration tools such as Airflow.
• Experience working with APIs for data ingestion and integration.
• Familiarity with version control systems (Git).
• Experience in fintech, financial services, or payments.
• Track record of leading cross-functional analytics projects from scoping through delivery.

Compensation

The expected salary range for this role is $114,192 - $142,740. 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.

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

We are seeking a GRC Automation Lead to join our GRC organization and build the technical foundation for how we scale our risk and compliance programs. In this role, you will lead the team that designs and implements automated workflows, data pipelines, and integrations that transform manual compliance processes into scalable engineering systems. 

This is a greenfield opportunity to establish the team, architecture, and integrations that will define how we approach governance, risk, and compliance at Anthropic. The core challenge is a data problem: compliance information lives across dozens of systems—cloud infrastructure, identity providers, HR platforms, ticketing tools, code repositories—and your job is to design systems that bring it together, normalize it, and make it actionable. Success in this role comes from understanding how systems connect and how data flows between them, not from writing code yourself.

At Anthropic, you'll also have a unique advantage: the ability to design AI-powered workflows where Claude acts as an extension of your team, handling tasks that would traditionally require additional headcount or manual effort. You'll need ingenuity to identify where agentic AI can accelerate evidence collection, interpret unstructured data, triage compliance gaps, and augment human judgment in risk assessments. Working closely with Security, IT, and Engineering teams, you'll translate compliance and regulatory requirements into solutions that support audit programs including SOC 2, ISO, HIPAA, and FedRAMP, building systems that combine traditional automation with AI capabilities to achieve scale that wouldn't otherwise be possible.

Responsibilities: 

• Lead the team that establishes foundational GRC processes and architecture. Design and build automated workflows for risk management and compliance, creating scalable systems that enable continuous monitoring as Anthropic grows.
• Build data pipelines that aggregate risk, control, and asset information from across our technology stack. This means solving hard data integration problems: mapping disparate schemas, handling inconsistent data quality, and creating unified views of compliance posture through dashboards and reporting tools.
• Inform GRC platform strategy and implementation: in partnership with other programs, evaluate, select, and deploy tooling that meets our compliance requirements.
• Translate written policies and compliance requirements into policy-as-code—working with Engineering and Security teams to express requirements as enforceable rules, automated checks, and continuous validation rather than static documents.
• Establish feedback loops between policy and implementation: surface where technical controls diverge from written requirements, identify where policies need to evolve based on infrastructure realities, and ensure that compliance requirements are expressed in terms engineers can act on.
• Design and deploy agentic AI workflows that extend team capacity, using Claude to serve as a virtual GRC analyst to automate evidence analysis, monitor control effectiveness, draft audit responses, interpret policy documents, and handle other tasks that require reasoning over unstructured information.
• Design and maintain integrations connecting GRC tooling with cloud infrastructure, identity management systems, HRIS platforms, ticketing systems, version control, and CI/CD pipelines—working with engineers to implement integrations that enable automated evidence collection and continuous compliance validation.
• Build and lead an AI-forward GRC engineering function as we scale: hiring team members, establishing practices, and defining the technical roadmap for governance and compliance automation at Anthropic.

You may be a good fit if you:

• Have 12+ years of total experience and 3-4+ years of experience managing technical individual contributors or systems-focused teams, with a proven track record of building or scaling small teams (2-5 people) in security, compliance, automation, or operations functions.
• Are a systems thinker first. You understand how complex environments work: how data flows between systems, where integration points exist, what breaks when systems don't talk to each other. Your strength is designing the right architecture and environment for security monitoring, not necessarily implementing it yourself.
• Have 5+ years of experience designing automated workflows, data pipelines, or system integrations, whether through traditional development, low-code platforms, GRC tools, or process automation. We care about your ability to solve integration problems more than your programming language proficiency.
• Have a relentless focus on data integration: you understand how to pull data from multiple sources, normalize it, join it meaningfully, and surface insights. You're comfortable reasoning about messy, inconsistent data and designing systems that handle edge cases gracefully.
• Understand APIs and integration patterns: REST APIs, webhooks, authentication flows, polling vs. push architectures, and can evaluate systems based on how well they expose data and support automation.
• Can work independently with minimal guidance, taking ownership of complex problems from design through implementation while managing ambiguity inherent in early-stage programs.
• Have strong analytical and problem-solving skills with attention to detail necessary for compliance work, balanced with pragmatism about risk-based prioritization in fast-paced environments.

Strong candidates may have:

• Experience designing or implementing AI-powered automation, agentic workflows, or LLM-based tooling in operational contexts.
• Experience with GRC platforms such as ServiceNow GRC, Vanta, Drata, OneTrust, RSA Archer, or similar tools including configuration, customization, and integration capabilities
• Familiarity with scripting languages (Python or similar) for automation tasks, API interactions, and data transformation.
• Prior experience in high-growth startup environments demonstrating ability to build scalable processes and adapt quickly to changing requirements and priorities
• Familiarity with Infrastructure as Code tools (Terraform, CloudFormation, Ansible) and DevSecOps practices including CI/CD pipeline integration and policy-as-code implementations.
• Familiarity with cloud platforms (AWS, GCP, Azure) and an understanding of how compliance-relevant data can be extracted from their APIs and logging systems.

Deadline to apply: None, applications will be received on a rolling basis.

About the Role:

We're looking for a Senior Staff Security Engineer to lead Gusto's edge and network security strategy, owning the design and operation of our Cloudflare WAF, DDoS protection, Zero Trust, and broader perimeter controls. The ideal candidate brings deep, hands-on Cloudflare expertise and a proven track record of hardening edge and network architectures at scale, including tuning WAF rulesets, defending through live DDoS events, and shipping Zero Trust rollouts engineers actually adopt. You think in terms of layered defense, measurable risk reduction, and automation over manual toil. In this role, you'll serve as a force multiplier across the security org, partnering with infrastructure and product teams to make high-impact architectural decisions that compound over time.

About the Team:

The Gusto's Enterprise Security Engineering team, a small but high-leverage group responsible for cloud security posture, edge and network defense, container security, secrets management, and endpoint protection across the company. The team runs a modern stack including Cloudflare, Wiz, CrowdStrike, Panther, and Tines, scaling impact through automation, IaC, and AI-augmented tooling. The work carries real stakes, protecting the payroll, benefits, and HR systems that hundreds of thousands of small businesses and their employees rely on every day. The team is engineering-first, with most of the roadmap living in code and a strong emphasis on partnering with infrastructure and product teams rather than gatekeeping them.

Here’s what you’ll do day-to-day:

• Design and operate Gusto's edge security stack including Cloudflare WAF, DDoS protection, Bot Management, WARP, Gateway, and Access, tuning rules against real traffic and shaping how engineers and operations teams reach internal systems securely.
• Own the network security perimeter across AWS and the edge: VPC design, Network Firewall, Shield, CloudFront, NACLs, and egress filtering, all codified in Terraform and Crossplane, observable, and consistently enforced.
• Develop policy-as-code patterns for WAF rules, network policies, and edge configuration so changes ship through pull requests with review, testing, and clean rollback paths.
• Build detections and alerting on edge and network telemetry including Cloudflare logs, VPC Flow Logs, and CloudTrail flowing into Panther, and lead incident response for perimeter and network events.
• Contribute broadly across the security engineering surface including cloud posture, container security, IAM, vulnerability management, and on-call, bringing a strong generalist instinct to wherever the work is most critical.
• Operate as an AI-native engineer, using Claude Code, MCP-driven tooling, and agentic workflows as a daily force multiplier across investigation, automation, and detection engineering.
• Prototype and ship agents, custom MCP servers, and LLM-assisted automations that compress security work from days to minutes and raise the bar for what one engineer can own.

Here’s what we're looking for:

• 10+ years of hands-on security engineering experience, with significant time owning edge, network, or perimeter security at scale.
• Deep, production-grade expertise with Cloudflare's security stack including WAF, DDoS, Bot Management, WARP, Gateway, and Access, covering rule tuning, incident response, and Zero Trust rollouts.
• Strong network architecture skills across edge and cloud: TLS/mTLS, segmentation, egress controls, DDoS resilience, and AWS networking including VPC, Network Firewall, Shield, CloudFront, and NACLs.
• Fluency with policy-as-code, Terraform, and CI/CD-first delivery of security controls; Crossplane or similar a plus.
• Solid generalist foundation across cloud security, IAM, container security, and detection engineering, with hands-on incident response experience on edge and network telemetry in a modern SIEM.
• AI-native working style with daily use of Claude Code or equivalent agentic tooling, and a track record of building AI-assisted workflows including custom MCP servers, agents, and LLM automations that compound team output.
• Excellent written and verbal communication; you can take a complex perimeter decision and explain the tradeoffs to a staff engineer, a PM, and a VP without changing the substance.
• Relevant certifications a plus including AWS Certified Advanced Networking Specialty, AWS Certified Security Specialty, Cloudflare Certified Security Associate/Professional, CKS, or equivalent.

About the Role

We’re looking to add a product-minded AI engineer to the team. The ideal candidate will have strong customer and product thinking, and the ability to be highly creative with LLM applications. We are looking for someone who can both rapidly prototype and architect production LLM pipelines.

Example workstreams include:

• Quickly iterating and developing proofs of concept (POCs) to explore the maximum potential of integrating AI into data and marketing workflows
• Making key decisions about the choice of AI architecture and frameworks
• Building production data agents that can make use of a company’s data to seamlessly answer analytics and data science questions about their users and marketing efforts
• Developing a deep understanding of user workflows to create seamless AI-assisted user experiences

We're seeking talented, intellectually curious, and motivated individuals interested in exploring the forefront of AI. While no prior experience with LLMs and AI is required, strong technical skills, particularly in backend architecture, and product intuition are essential for understanding and executing these projects from start to finish.

This is a senior role, but we focus on impact and potential for growth more than years of experience. The salary range for this position is $180,000 - $320,000 USD per year, which is location independent in accordance with our remote-first policy.

Interview Process

Our interview process focuses on evaluating fit for the most important dimensions of the role: product sense and creativity with LLMs, ability to architect backend systems, and alignment with Hightouch’s values. Notably, we don’t do any programming interviews as we believe they are low signal to noise and aren’t a good evaluation mechanism.

• Recruiter Screen [30m]: Introductory call with our recruiting team to get to know each other and see if the role could be a good mutual fit.
• System Design Screen [60m]: Designing a data processing feature end-to-end.
• Hiring Manager Interview [30m]: Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.
• Agent Building Systems Interview [75m]: Work with the interviewer to architect an agentic system at a conceptual level. The problem will be at a pretty high level - and have both product and customer requirements as well as technical.

We have limited inbound applications to one application per candidate. You will be auto-rejected if you apply to multiple roles. Please only apply to the position you are most qualified for.

We are looking for a Staff Forward Deployed Engineer who is passionate about solving complex cloud infrastructure challenges in the fast-growing AI/ML space. This is a high-impact position designed to be the "technical tip of the spear" for our most strategic AI-Native (ANE) customers.

As an FDE, you will sit at the intersection of Product Engineering and Customer Implementation. You will be embedded directly with our most strategic customers to drive transformational AI adoption. You will collaborate closely with customer teams to ship advanced AI applications that solve real world business problems.

You will build the tools, migration scripts, and AI starter kits that don't just solve one customer's problem, but scale the entire DigitalOcean AI Cloud ecosystem. Your mission is to accelerate "time-to-inference" in production at scale and serve as a critical feedback loop, ensuring our product roadmap is informed by the world's most demanding AI workloads.

What you’ll do:

• Embedded Customer Engineering: Partner deeply with strategic ANEs to manage complex migrations, build production-ready PoCs, and execute hands-on application builds on our GPU infrastructure.
• Build Scalable Assets & Tooling: Operationalize field learning by building migration planners, reusable systems, benchmarking frameworks, model optimization agents, and deployment automation scripts (e.g., Terraform/Pulumi) that standardize how AI workloads are deployed and tuned across DigitalOcean.
• Develop AI "Starter Kits": Create pre-built GenAI agents, solution templates, and notebooks (e.g., LangGraph, CrewAI) tailored to popular and advanced business needs.
• Drive the Product Roadmap: Act as the frontline technical voice for CPTO, surfacing architectural gaps and edge cases to inform core product development. Transition field-built tools into native product features.
• New Tech Validation: Lead the early testing and integration of emerging AI frameworks to ensure a first-class developer experience on DigitalOcean.
• Partner & GTM Enablement: Co-develop delivery frameworks with Strategic and Technical partners to enable repeatable, high-quality deployments at scale.
• Market Leadership: Produce field-tested demo kits and deployment guides that support new product releases with practical, validated assets.

Key Metrics

• Customer Adoption: Number of high-impact production workloads landed; reduction in average "time-to-production"; and Pilot-to-Production conversion rates.
• Asset & Tooling Delivery: Number of reusable tools (agents, scripts, playbooks) delivered per quarter and their subsequent adoption rate by the crossfunctional teams and customers.
• Field Enablement & Scale: Measured by the successful handoff and execution of FDE-defined deployment patterns to technical partners and the internal adoption rate of FDE playbooks across all customer-facing technical teams.
• Product Influence: Number of roadmap features or architectural changes directly attributed to FDE feedback and validated customer hypotheses.

What You’ll Add to DigitalOcean:

• AI/ML Architecture & Domain Expertise: Significant experience in the AI/ML lifecycle, specifically hosting large language or multimodal models using inference engines like vLLM, SGLang, or Modular. You have a deep understanding of common LLM architectures and optimization techniques (e.g., continuous batching, quantization).
• Distributed Systems & Infrastructure Mastery: Act as the subject matter expert on modern GPU families (NVIDIA/AMD) and their software stacks (CUDA, ROCm, TensorRT, OpenAI Triton). Expert proficiency in Kubernetes (K8s) and the design of distributed systems, including microservices, messaging systems, databases, and Infrastructure as Code. You can seamlessly integrate AI workloads with core cloud services (Networking, VPC, Storage, Compute).
• High-Performance Inference & Hardware Optimization: Hands-on experience with distributed inference serving frameworks (e.g., llm-d, NVIDIA Dynamo, or Ray Serve). You understand GPU-level optimization and have experience with interconnect technologies like NVlink, XGMI, or RoCE to maximize hardware efficiency.
• Software Engineering Foundation: Strong production coding skills in Python or Go with the ability to build high-quality tools, automation, and internal assets.
• Data-Driven Performance Insight: Proven ability to benchmark AI infrastructure and perform GPU utilization tuning to optimize customer ROI and workload performance.
• Consultative Execution: Proven ability to establish instant technical credibility with CTOs and Lead Architects while managing high-stakes technical migrations.

Preferred

• Experience with emerging AI orchestration frameworks like LangGraph, CrewAI, or LlamaIndex.
• A background in technical consulting or "Forward Deployed" roles at high-growth AI or infrastructure companies.
• Experience building and scaling "Center of Excellence" models or partner delivery frameworks.
• Active contributor to open-source AI projects or technical communities.

Compensation Range: 

• $195,000 - $239,000

*This is a remote role

JR: 2026-7748

#LI-Remote

About the role

Anthropic is hiring a Lead for our Talent Development & Enablement team to help build the systems that help Ants (particularly managers) thrive. This is a build role: you'll partner directly with the team lead to think holistically about talent development across Anthropic, with a strong primary focus on the manager and leader audience.

You'll bring deep instructional design expertise, exceptional facilitation skills, and a strong point of view on how to grow people, and managers in particular, in an environment of unprecedented growth and change. You'll design across modalities (self-service, live virtual, in-person) and across audiences (new hires, ICs, first-time managers, experienced leaders, executives), and you'll treat AI as a chance to reimagine what talent development can be, not retrofit it onto what already exists.

This role suits someone who is energized by extreme ambiguity, ships fast, and wants to invent the playbook rather than execute someone else's. You'll be one of the first specialist hires on a new team, which means your point of view will shape what we build and how we measure success.

Key responsibilities

• Design and ship talent development experiences end-to-end — from needs analysis through facilitation and measurement 
• Build innovative, Claude-enabled learning experiences that Ants actually use and find valuable, across audiences and modalities
• Facilitate live sessions for managers and senior leaders, including workshops and offsites; flex into broader Ant audiences when the work calls for it
• Partner closely with cross-functional teams to integrate learning into the broader Ant and manager experience
• Develop a clear point of view on what makes Ants — and managers in particular — effective at Anthropic and design against that
• Make the work visible — clearly communicate the value, outcomes, and strategic relevance of programs to the broader organization
• Iterate rapidly based on feedback; ship a v1 in days or weeks, not quarters

Minimum qualifications 

• Deep experience in learning & development, instructional design, or organizational development at high-growth tech companies
• Have personally designed and shipped talent development programs at scale, with strong examples in manager and leader development specifically — built from first principles, not assembled from vendor content
• Are an exceptional facilitator with experience holding the room with senior audiences, including VPs and executives
• Have demonstrable AI/LLM fluency in learning design — you have built or shipped AI-enabled learning experiences and have a point of view on what AI changes about how people learn
• Can design across modalities (self-service, live virtual, in-person) and across audiences (new hires, ICs, first-time managers, experienced leaders, executives)
• Are energized by building from scratch in fast-moving, ambiguous environments and can name specific meaningful ships you delivered in short windows
• Can connect the dots across the full talent lifecycle and articulate how your work ladders up to broader company strategy

Preferred qualifications

• 8-10+ years in learning & development, instructional design, or organizational development at high-growth tech companies
• Experience building Claude- or LLM-powered learning tools
• Background in instructional design, adult learning theory, or organizational psychology
• Experience designing development programs for technical audiences (engineering, research) at the IC and manager level
• Experience preparing organizations and their managers for significant change (rapid scaling, role evolution, reskilling)

About this role

Faire leverages the power of machine learning and data insights to revolutionize the wholesale industry, enabling local retailers to compete against giants like Amazon and big box stores. At Faire, the Data Science team is responsible for creating and maintaining a diverse range of algorithms and models that power our marketplace. We are dedicated to building machine learning models that help our customers thrive. 

As a member of the Brand Data Science team working on Listing Quality, you will be responsible for improving the quality of product listings to help retailers find and evaluate products on Faire. You will use ML and AI to tackle critical challenges, such as enhancing image and text quality, extracting structured product attributes, and accurately identifying duplicates and product variants. You will leverage deep learning, multi-modal LLMs, and human-in-the-loop training to create high performance solutions. This space has been evolving rapidly with advancements in AI and you will be at the forefront of applying the latest technology to drive real-world impact. You will independently design and implement solutions and work with the cross-functional Listing Quality pod, including product, design, engineering, analytics, and operations, to solve problems end-to-end.

What you’ll do

• Drive data science vision, strategy, and execution on Listing Quality, using ML and AI solutions to improve the quality of Faire’s product listings.
• Use deep learning, LLM fine tuning, and human-in-the-loop training to automatically detect and address issues with high accuracy.
• Act as a lead on the cross-functional Listing Quality pod, thinking end-to-end about brand and retailer experiences.

Qualifications

• 3+ years of industry experience using machine learning to solve real-world problems
• Experience with relevant business problems (e.g. e-commerce)
• Experience with relevant technical methods (e.g. LLM fine tuning, deep learning, or human-in-the-loop machine learning)
• Strong programming skills
• An excitement and willingness to learn new tools and techniques
• The ability to design and implement ML solutions without supervision
• Strong communication skills and the ability to work in a highly cross-functional team 

Great to Haves:

• Master’s or PhD in Computer Science, Statistics, or related STEM fields is highly recommended
• Previous experience in listing quality for e-commerce
• Previous experience in supervised fine tuning of multi-modal LLMs
• Experience deploying and optimizing LLM inference systems at scale (10B+ tokens), with focus on cost efficiency and product impact

Salary Range

Canada: the pay range for this role is $180,000 to $247,500 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.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

About this role

Faire leverages the power of machine learning and data insights to revolutionize the wholesale industry, enabling local retailers to compete against giants like Amazon and big box stores. At Faire, the Data Science team is responsible for creating and maintaining a diverse range of algorithms and models that power our marketplace. We are dedicated to building machine learning models that help our customers thrive. 

As a member of the Brand Data Science team working on Listing Quality, you will be responsible for improving the quality of product listings to help retailers find and evaluate products on Faire. You will use ML and AI to tackle critical challenges, such as enhancing image and text quality, extracting structured product attributes, and accurately identifying duplicates and product variants. You will leverage deep learning, multi-modal LLMs, and human-in-the-loop training to create high performance solutions. This space has been evolving rapidly with advancements in AI and you will be at the forefront of applying the latest technology to drive real-world impact. You will independently design and implement solutions and work with the cross-functional Listing Quality pod, including product, design, engineering, analytics, and operations, to solve problems end-to-end.

What you’ll do

• Drive data science vision, strategy, and execution on Listing Quality, using ML and AI solutions to improve the quality of Faire’s product listings.
• Use deep learning, LLM fine tuning, and human-in-the-loop training to automatically detect and address issues with high accuracy.
• Act as a lead on the cross-functional Listing Quality pod, thinking end-to-end about brand and retailer experiences.

Qualifications

• 3+ years of industry experience using machine learning to solve real-world problems
• Experience with relevant business problems (e.g. e-commerce)
• Experience with relevant technical methods (e.g. LLM fine tuning, deep learning, or human-in-the-loop machine learning)
• Strong programming skills
• An excitement and willingness to learn new tools and techniques
• The ability to design and implement ML solutions without supervision
• Strong communication skills and the ability to work in a highly cross-functional team 

Great to Haves:

• Master’s or PhD in Computer Science, Statistics, or related STEM fields is highly recommended
• Previous experience in listing quality for e-commerce
• Previous experience in supervised fine tuning of multi-modal LLMs
• Experience deploying and optimizing LLM inference systems at scale (10B+ tokens), with focus on cost efficiency and product impact

Salary Range

San Francisco: the pay range for this role is $211,000 to $290,500 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.

Tax Planning Senior Manager

The Global Tax Team is seeking a dedicated tax professional who will play a hands-on role in a growing tax function. As Reddit continues to mature as a public company, this person will play a critical role in our strategic tax planning function, acting as a high-impact advisor to Finance and other cross functional teams. In keeping with Reddit values, the successful leader in this role is someone who operates with an open mindset and “values the human” in all interactions. This position will report to the Global Tax Director.

 Responsibilities:

• Lead the Company’s tax planning strategy and execution
• Own the global cash tax and ETR forecasting process and collaborate with Finance partners to model tax impact of business trends.
• Drive efforts to maximize tax incentives and mitigate risk
• Oversee global tax due diligence for M&A transactions and partner with Corporate Development team to model deal structures and manage post-acquisition integration
• Review and negotiate commercial agreements for tax terms and indemnifications
• Assist the provision team with preparation of quarterly and annual income tax provisions
• Design and implement tax process improvements/tax automation
• Monitor global tax legislative developments and research tax laws and policies to evaluate their tax impact
• Collaborate with business partners on ad hoc projects and provide guidance on tax related matters

Qualifications:

• Bachelor’s Degree in Accounting, Finance, or related field required, masters in tax (MST or LLM) preferred
• JD and/or CPA required
• 9+ years of corporate tax experience with at least 5 years of public accounting experience. Must have in-house experience, preferably in the tech sector
• Demonstrated expertise in accounting for income taxes, M&A and deal review processes, and US international tax regulations
• Proficiency in Excel, MS Office, and Google Workspace (e.g. Google Sheets and Slides) required, experience with Alteryx a plus
• Able to work independently under time pressure, prioritize a high workload, and meet strict deadlines
• Possess excellent attention to detail, organizational, problem solving, and prioritization skills 
• Excellent verbal and written communication skills and the ability to work collaboratively across teams in a remote environment
• Team player with a positive attitude and open mindset, not afraid to roll up your sleeves and work in a dynamic environment

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

About the Director, AI Enablement at Headspace:

Headspace is building an AI-first operating model grounded in member safety, privacy, and measurable business impact. As Director, AI Enablement, you will define and drive the strategy that helps Headspace adopt AI thoughtfully, responsibly, and at scale across the enterprise.

This is a highly visible role, reporting to the COO/Founder, with the opportunity to shape how teams across Headspace evaluate, build, govern, and scale AI-powered workflows, tools, and operating practices. You will partner closely with executive leaders and senior stakeholders across Product, Engineering, Legal, Privacy, Security, Procurement, Care, and Go-to-Market to translate company priorities into an AI roadmap that delivers meaningful outcomes for our teams and the members we serve.

This role is ideal for a strategic and hands-on leader who can move fluidly from vision to execution: setting direction, building operating mechanisms, developing talent, and leading cross-functional change in a fast-moving environment. You bring strong judgment, a bias toward learning, and a commitment to responsible AI. You’re willing to challenge defaults, run smart experiments, and unblock teams while staying inside responsible AI guardrails. In doing so, you will help Headspace build the capabilities, guardrails, and cultural momentum needed to make AI a durable advantage in service of our mission to provide every person access to lifelong mental health support.

What you will do:

• Define and lead Headspace’s enterprise AI enablement strategy, roadmap, and operating model, aligning investments and priorities to company goals, member outcomes, and measurable business impact.
• Partner with the COO/Founder, executive team, and senior leadership team to identify high-value opportunities for AI across the business and turn them into a focused, transparent, and scalable portfolio of work.
• Own AI intake and prioritization: triage requests, refine problem statements, size value, and maintain a transparent queue.
• Lead AI initiatives end-to-end (discovery → pilot → launch), aligning scope, milestones, success metrics, and stakeholders. Own the outcome and drive real impact by building.
• Lead a high-impact team, while also leading/influencing crossfunctionally to coach talent and build the organizational muscle needed to scale AI adoption, experimentation, and responsible execution over time.
• Drive cross-functional alignment with senior stakeholders across Product, Engineering, Legal, Privacy, Security, Procurement, and functional leadership to accelerate delivery while managing risk appropriately.
• Define the enterprise standards, frameworks, and guardrails for AI vendor evaluation, implementation, and use, including build-versus-buy recommendations, governance pathways, and responsible AI practices.
• Drive Headspace’s AI enablement capability through scalable programs such as leadership engagement, functional playbooks, training, office hours, and champion networks that increase fluency and adoption across teams.
• Oversee a portfolio of AI initiatives and ensure teams are delivering against clear business cases, adoption metrics, operational improvements, and lessons learned that inform future investment.
• Unblock high-leverage technical/integration work and identify next-wave AI opportunities to scale adoption and cultural change.
• Deliver executive-ready communication, recommendations, and reporting on AI strategy, portfolio health, risks, trade-offs, and realized impact.
• Act as a strategic thought partner and visible leader for AI adoption at Headspace, helping shape a culture that is ambitious, practical, and grounded in safety, privacy, and our values.

Required Skills:

• 7+ years of experience in product management/operations, technical program management, business operations, strategy, digital transformation, or related roles delivering software/AI projects end-to-end.
• Significant experience leading cross-functional, enterprise-level transformation work with demonstrated impact across multiple functions or business units.
• Experience setting strategy, building roadmaps, and establishing operating mechanisms for AI, automation, or other emerging technology initiatives in a fast-paced environment.
• Demonstrated success influencing and partnering with executive leaders and senior stakeholders in a highly matrixed organization.
• People leadership experience, including building, managing, mentoring, and developing high-performing teams and/or standing up a new function or capability.
• Strong aptitude of AI concepts (LLMs/agents, common tradeoffs, prototype→pilot→scale lifecycle) and ability to translate technical constraints into clear timelines and decision points.
• Ability to design, evaluate, and iterate on prompts for LLM-based workflows; essential for building and reviewing AI agents
• Ability to define, interpret, and act on adoption and outcome metrics; comfort with dashboards (Looker, Tableau, etc.)
• Experience evaluating AI use cases through a business lens, including feasibility, value, risk, adoption readiness, and return on investment.
• Experience partnering with Legal, Privacy, Security, and Procurement or equivalent functions to implement AI responsibly in regulated or high-trust environments.
• Strong analytical and operational rigor, with the ability to define success metrics, assess outcomes, and make clear prioritization decisions.
• Excellent written and verbal communication skills, with the ability to synthesize complexity into crisp recommendations, executive updates, and change-ready messaging.

Preferred Skills:

• Experience leading AI enablement, transformation, or internal adoption work in healthcare, digital health, wellness, or another privacy- and safety-sensitive environment.
• Experience building enterprise enablement programs such as champion networks, training curricula, playbooks, and adoption frameworks.
• Experience with vendor selection, contract evaluation, and budget or portfolio management for AI tools and platforms.
• Experience balancing strategic leadership with hands-on prototyping, workflow design, prompt iteration, or early-stage solution development.
• Familiarity with change management approaches that help organizations adopt new tools and ways of working at scale.
• Relevant certifications in program management, product management, change management, or AI-related disciplines (PMP/CSM/AI certificates).

Location:

We are currently hiring this role remotely in the US and Hybrid for San Francisco (SF). Candidates must permanently reside in the US full-time.
For candidates with a primary residence in the greater SF area, this role will follow our hybrid model. You’ll work 3 days per week from our office, allowing for impactful in-office collaboration and connection, while enjoying the flexibility of remote work for the rest of the week. Your recruiter will share more details about our hybrid model. 

Pay & Benefits:

The anticipated new hire base salary range for this full-time position is $138,600-$220,000 + equity + benefits.

For roles based in San Francisco, New York City, or Seattle, a separate salary range of $177,100-$250,000 applies, consistent with our location-based compensation philosophy.

Our salary ranges are based on the job, level, and location, and reflect the lowest to highest geographic markets where we are hiring for this role within the United States. Within this range, individual compensation is determined by a candidate’s location as well as a range of factors including but not limited to: unique relevant experience, job-related skills, and education or training. 
Your recruiter will provide more details on the specific salary range for your location during the hiring process.

At Headspace, base salary is but one component of our Total Rewards package. We’re proud of our robust package inclusive of: base salary, stock awards, comprehensive healthcare coverage, monthly wellness stipend, retirement savings match, lifetime Headspace membership, generous parental leave, and more. Additional details about our Total Rewards package will be provided during the recruitment process. 

About Headspace

Headspace exists to provide every person access to lifelong mental health support. We combine evidence-based content, clinical care, and innovative technology to help millions of members around the world get support that’s effective, personalized, and truly accessible whenever and wherever they need it.

At Headspace, our values aren’t just what we believe, they’re how we work, grow, and make an impact together. We live them daily: Make the Mission Matter, Iterate to Great, Own the Outcome, and Connect with Courage. These values shape our decisions, guide our collaborations, and define our culture. They’re our shared commitment to building a more connected, human-centered team—one that’s redefining how mental health care supports people today and for generations to come.

Why You’ll Love Working Here:

• A mission that matters—with impact you can see and feel
• A culture that’s collaborative, inclusive, and grounded in our values
• The chance to shape what mental health care looks like next
• Competitive pay and benefits that support your whole self

How we feel about Diversity, Equity, Inclusion and Belonging:

Headspace is committed to bringing together humans from different backgrounds and perspectives, providing employees with a safe and welcoming work environment free of discrimination and harassment. We strive to create a diverse & inclusive environment where everyone can thrive, feel a sense of belonging, and do impactful work together. 

As an equal opportunity employer, we prohibit any unlawful discrimination against a job applicant on the basis of their race, color, religion, gender, gender identity, gender expression, sexual orientation, national origin, family or parental status, disability*, age, veteran status, or any other status protected by the laws or regulations in the locations where we operate. We respect the laws enforced by the EEOC and are dedicated to going above and beyond in fostering diversity across our workplace. 

*Applicants with disabilities may be entitled to reasonable accommodation under the terms of the Americans with Disabilities Act and certain state or local laws. A reasonable accommodation is a change in the way things are normally done which will ensure an equal employment opportunity without imposing undue hardship on Headspace. Please inform our Talent team by filling out this form if you need any assistance completing any forms or to otherwise participate in the application or interview process.

Headspace participates in the E-Verify Program.

Privacy Statement

All member records are protected according to our Privacy Policy. Further, while employees of Headspace (formerly Ginger) cannot access Headspace products/services, they will be offered benefits according to the company's benefit plan. To ensure we are adhering to best practice and ethical guidelines in the field of mental health, we take care to avoid dual relationships. A dual relationship occurs when a mental health care provider has a second, significantly different relationship with their client in addition to the traditional client-therapist relationship—including, for example, a managerial relationship.  

As such, Headspace requests that individuals who have received coaching or clinical services at Headspace wait until their care with Headspace is complete before applying for a position. If someone with a Headspace account is hired for a position, please note their account will be deactivated and they will not be able to use Headspace services for the duration of their employment. 

Further, if Headspace cannot find a role that fails to resolve an ethical issue associated with a dual relationship, Headspace may need to take steps to ensure ethical obligations are being adhered to, including a delayed start date or a potential leave of absence. Such steps would be taken to protect both the former member, as well as any relevant individuals from their care team, from impairment, risk of exploitation, or harm.

For how how we will use the personal information you provide as part of the application process, please see: https://www.headspace.com/applicant-notice

#LI-Hybrid

SLSQ127R420

While candidates in the listed location(s) are encouraged for this role, candidates in other locations will be considered.

As the GTM Digital Native Programs Leader, you will operate as the de facto COO and scale pipeline and consumption  for th high growth Databricks’ Digital Native vertical. You will partner in a "two-in-a-box" model with the Industry GTM Leader to translate  strategy into durable, repeatable commercial execution across a rapidly scaling global field organization. You will work closely with sales and field engineering leaders to ideate, build, execute and scale global programs. 

You will play a critical role in shaping Databricks’ growth trajectory. You will be part of a lean GTM leadership that powers sellers to deliver measurable business outcomes through scale & leverage. You will be the lead orchestrator for your industry vertical, at the center of a mandate to inspire executives, drive industry outcomes, and unleash Databricks’ partner ecosystem in the high-growth SaaS space. 

The Impact You Will Have

Strategy & Operating Model

• Operating Model Ownership: Own and implement end-to-end op-model (across Sales, FE, Product, Engineering, Developer Relations, Partners) in a consistent global approach.
• Product-GTM Strategy: Inform the annual Digital Native strategy by synthesizing product telemetry and engineering roadmaps with field signals to identify Big Bets in the digital natives space.
• Data-Driven Product Feedback: Synthesize learnings from internal (field, product, engineering, developer relations) & external (customer, partner, developers) stakeholders into durable best practices 

Market Presence & Influence

• Internal Executive Influence: Wield two-way influence on Databricks’ Field (Sales, FE, Business Dev), Product & Engineering leadership: i.e., reflect industry signals in product roadmaps, as well as translate product activation goals into verticalized industry programs
• Market Presence: Own and engage with technical & business customer and partner executives, build market presence to uncover signals on technical friction, business value and Databricks differentiators.

• Industry Evangelism: Serve as a visible evangelist & thought leader (e.g., open source advocacy, scaling AI adoption), representing Databricks’ at key customer, partner, industry and developer forums

Execution & Accountability

• Full-Funnel Program Design: Select & design the industry’s programs across products (and verticalize product programs) across the full funnel: demand generation → pipe creation & progression → consumption
• Asset Creation: Create high-quality and scalable program assets that will be used by the full field (Sales, FE, BD, Marketing, Ecosystem) at scale

• Field Activation & Community: Drive continuous field adoption of program assets and outcomes by cross-pollinating best practices across product and field teams in high-impact community cadences.
• Ecosystem Integration: Collaborate with the partnership, ISV, and Data Marketplace teams to drive adoption of a seamless, interconnected data-sharing ecosystem.

• Outcome Measurement: Deliver commercial outcomes by establishing measurement and monitoring systems that track product telemetry, industry & partner program performance.

• Executive Accountability: Drive cross-functional leadership accountability for program adoption and outcomes through continuous monitoring and executive influence.

What We Look For

Strategic & Operational Mindset

• COO Mindset: Proven ability to act as the operational backbone of a business vertical, focusing on pipeline health, organizational efficiency, and execution.
• Strategic Execution: A track record of defining and translating complex industry strategies into durable, "field-ready" commercial motions for large organizations.
• Programmatic Vision: Experience developing global, scalable GTM frameworks - moving beyond "one-off" deals to create a scalable engine for growth.

Technical & Domain Expertise

• Technical Chops: Technical knowledge on Databricks critical for this market. Comfort with discussing  AI & LLM, Lakehouse, Lakebase, governance 

• Industry POV: A strong point of view on macro trends in high-growth SaaS and key persona triggers, helping to shape our industry advantage against peers.

• Open Source Knowledge: A strong understanding of the open-source ecosystem and how it integrates with proprietary enterprise platforms.
• AI Builder Experience: Proven track record of hands-on experience building AI systems (MLOps, GenAI apps, or large-scale predictive models).

Execution & Analytical Skills

• Full-Funnel Expertise: Deep understanding of sales play mechanics, including lead generation, pipeline progression, and consumption drivers.

• Scale Orientation: A "multiplier" mindset with a passion for activating large-scale field organizations and cross-pollinating best practices.
• Entrepreneurial Spirit: A "doer" mindset with the ability to operate in a fast-paced, ambiguous environment and drive results across cross-functional teams.

Leadership & Scale

• Influence Without Hierarchy: Ability to influence & drive outcomes across senior executives & global teams via influence, clarity & presence versus direct reporting lines.
• Cross-Functional Orchestration: Skill in collaborating with Product, Engineering, and BD leadership to align product roadmap shifts with commercial requirements specific to high-growth SaaS customers.

As the leading data and evaluation partner for frontier AI companies, Scale is dedicated to advancing the evaluation and benchmarking of large language models (LLMs). We are building industry-leading LLM evals, setting new standards for model performance assessment. Our mission is to develop rigorous, scalable, and fair evaluation methodologies to drive the next generation of AI capabilities.

Our Research teams work with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. As a Staff Machine Learning Research Scientist on the LLM Evals team, you will lead the development of novel evaluation methodologies, metrics, and benchmarks to measure the capabilities and limitations of frontier LLMs. You will help define what "good" looks like in generative AI, driving research that informs both our internal roadmap and the broader research community. This role is critical for designing and executing a roadmap that defines best practices in data driven AI development and will accelerate the next generation of generative AI models in partnership with top foundational model labs.

You will:

• Drive research on the effectiveness and limitations of existing LLM evaluation techniques.
• Design and develop novel evaluation benchmarks for large language models, covering areas such as instruction following, factuality, robustness, and fairness. 
• Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects.
• Collaborate with internal teams and external partners to refine metrics and create standardized evaluation protocols.
• Implement scalable and reproducible evaluation pipelines using modern ML frameworks.
• Publish research findings in top-tier AI conferences and contribute to open-source benchmarking initiatives.
• Mentor and guide research scientists and engineers, providing technical leadership across cross-functional projects.
• Stay deeply engaged with the ML research community, tracking emerging work and contributing to the advancement of LLM evaluation science.
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

Ideally you'd have:

• 5+ years of hands-on experience in large language model, NLP, and Transformer modeling, in the setting of both research and engineering development
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience tech leading a team of research scientists and research engineers
• Excellent written and verbal communication skills
• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
• Previous experience in a customer facing role.

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 9 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent investment from Meta, we are doubling down on building out state of the art post-training algorithms to reach the performance necessary for complex agents in enterprises around the world. 

The Enterprise ML Research Lab works on the front lines of this AI revolution. We are working on an arsenal of proprietary research, tools, and resources that serve all of our enterprise clients. As a Staff Agent Post-Training MLRE, you will build out our next-gen Agent RL training platform. You’ll build out the platform that will train best-in-class Agents that achieve state of the art results on real enterprise use-cases. 

You’ll integrate cutting edge research into our training stack, enabling MLREs on the Enterprise AI team to deploy use-cases ranging from next-generation AI cybersecurity firewall LLMs to training foundation healthtech search models. If you are excited about shaping the future of the modern GenAI movement, we would love to hear from you!

You will: 

• Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers. 
• Research cutting edge algorithms to integrate directly into our training stack.
• Design solutions that enable complex multi-agent systems to directly learn from both process + outcome based rewards.

Ideally you’d have:

• 5+ years of LLM training in a production environment
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
• PhD or Masters in Computer Science or a related field

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations.

About Data Engine

Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.

Our Approach 

As part of the interview process, you’ll be considered for opportunities across several teams within the GenAI Engineering organization, based on your interests, expertise, and business needs. Potential team placements include Allocation, Growth, Frontier Data, Trust & Safety, Pay, Operator, or Tasking Experience. Together, these teams power Scale’s AI data operations - from building high-impact datasets that push the boundaries of LLM capabilities, to optimizing contributor onboarding and incentives, to safeguarding data integrity through advanced trust, safety, and security measures. They work at the intersection of ML, operations, and analytics to ensure we deliver the highest-quality data at scale.

Responsibilities:

• Design, build, and maintain robust, scalable systems across the full stack, including front-end, back-end, and infrastructure layers
• Implement high-impact features using modern technologies such as TypeScript, React, Node.js, MongoDB, Elasticsearch, and Temporal
• Collaborate closely with internal operators (your users are your neighbors) to identify bottlenecks and ship fast, pragmatic solutions
• Own core systems critical to our contributor platform, with direct impact on Scale’s GenAI data pipeline and business outcomes
• Architect and scale infrastructure capable of handling millions of tasks per week with high reliability and low latency
• Partner cross-functionally with ML teams, Forward Deployed Engineers, and Product to ensure data quality and operational excellence
• Contribute to a strong engineering culture while setting best practices for teammates through mentorship, code reviews, and process improvements

Requirements:

• 5+ years of software engineering experience, ideally in high-growth, product-focused environments
• Proven track record of shipping production systems at scale
• Drive reliability and performance across critical infrastructure systems, ensuring our platforms scale predictably and operate with high availability.
• Strong technical depth in one or more areas: front-end frameworks, distributed systems, data infrastructure, or developer tooling
• Experience working across the stack, ideally with React, TypeScript, Node.js, Python, MongoDB, Elasticsearch, and/or Temporal
• Strong product sense and ability to translate ambiguous problems into technical solutions
• Comfortable working in a fast-paced, high-ownership environment with a bias toward execution
• Excited to join a dynamic hybrid team based in San Francisco or New York City

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform providing APIs for knowledge retrieval, inference, evaluation, and more. We are seeking a strong Senior Full-Stack Engineer to help us build, scale, and refine our rapidly growing product. The ideal candidate is deeply grounded in software engineering best practices and experienced in developing and scaling modern web applications end-to-end. You will work across the stack—from React/TypeScript frontends to Python-based backends—while integrating with LLMs and machine learning systems.

You will solve complex challenges in scalability, reliability, and product experience while owning significant product areas in a fast-paced environment.

What You’ll Do

• Own major full-stack product areas, driving features from design through production deployment.
  
  
• Build modern frontend experiences using React and TypeScript, ensuring performance, usability, and responsiveness.
  
  
• Develop reliable backend services in Python, working with distributed systems, data pipelines, and ML/LLM components.
  
  
• Integrate with LLMs, vector databases, and AI infrastructure to power intelligent product experiences.
  
  
• Deliver experiments and new features quickly, maintaining high quality and tight feedback loops with customers.
  
  
• Collaborate across product, ML, and infrastructure teams to shape the direction of Scale GP.
  
  
• Adapt quickly—learning new technologies, frameworks, and tools as needed across the stack.
  
  

Ideal Experience

• 5+ years of full-time engineering experience, post-graduation.
  
  
• Strong experience developing full-stack applications using React, TypeScript, and Python.
  
  
• Experience scaling or shipping products at high-growth startups.
  
  
• Familiarity with LLMs, vector databases, embeddings, or other modern AI tooling (tinkering or production experience welcome).
  
  
• Proficiency with SQL and modern API development.
  
  
• Experience with Kubernetes, containerization, and microservice architectures.
  
  
• Experience working with at least one major cloud provider (AWS, GCP, or Azure).

Scale GP is Scale’s enterprise Generative AI platform, providing APIs and infrastructure for knowledge retrieval, inference, evaluation, and intelligent automation. We power mission-critical workflows for leading enterprises by helping teams turn complex data and models into reliable, production-ready AI systems.

We’re forming a new Agentic Data Products team focused on building the next generation of agent-powered tools that ground AI in real operational workflows. Our goal is to help enterprises demystify their data layers and deploy intelligent, agentic systems that can reason over data, take action, and deliver measurable outcomes.

This is a 0→1 build team. We’re looking for a sharp, product-minded Senior Engineer who thrives in ambiguity, moves quickly, and enjoys building new systems from scratch alongside customers and cross-functional partners. You’ll work closely with product, forward-deployed engineers, data scientists, and applied AI teams to turn real-world problems into scalable, production solutions.

If you like shipping fast, owning outcomes, and working across the stack—from polished frontends to distributed backends to LLM integrations—this role is for you.

What You’ll Do

• Own major full-stack product areas, driving features from concept and design through production deployment
• Build intuitive, high-performance frontend experiences using React + TypeScript
• Develop reliable backend services in Python, working with distributed systems, data pipelines, and AI/ML infrastructure
• Integrate LLMs, vector databases, and agentic frameworks to power intelligent workflows and decision-making systems
• Ship quickly through tight experimentation loops while maintaining high quality and reliability
• Help define the technical direction and architecture of a brand-new team and product surface
• Adapt across the stack and learn new tools as needed to solve real problems end-to-end

Ideal Experience

• 5+ years of full-time software engineering experience
• 0-1 product build experience 
• Familiarity with LLMs, embeddings, vector databases, or modern AI data products/tools 
• Experience with distributed systems and cloud-based architectures
• Prior experience mentoring or leading team 

What We Value

• Strong product intuition and customer empathy
• Bias toward action and rapid iteration
• Ownership mentality — you see problems through to outcomes
• Comfort collaborating across engineering, product, data science, and applied AI
• Excitement about building agentic systems that make AI genuinely useful in the real world

Please note that in order to maintain integrity to our Scale titling philosophy, we do not internally use titles such as “senior,” but have levels to reflect seniority. Our Talent Acquisition team works closely with our employees to provide them the opportunity to grow their careers and demonstrate the scope of work in other ways aside from job titles. If you have further questions, please direct them to your recruiter and/or hiring manager, who can provide more insight.

The Enterprise ML team works on the front lines of the AI revolution, partnering deeply with customers to identify high-impact business problems and build cutting-edge AI systems using Scale’s proprietary research, data, and infrastructure—unlocking domain expertise through high-quality data and expert feedback.

As Director of Enterprise ML, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.

This role is ideal for a leader who thrives in ambiguity, understands both frontier GenAI capabilities and their limitations, and is motivated by turning research into durable, production-ready systems.

What You’ll Do

• Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).  
• Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.
• Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.
• Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.
• Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.
• Stay deeply connected to the research community, understanding major trends, and helping set them.
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

What We’re Looking For

Core Qualifications

• 8+ years of hands-on research experience (PhD or equivalent preferred) in machine learning, deep learning, generative models, agent/rl systems or related domains.
• A strong track record of research excellence, including publications in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience leading or managing research teams. You’re excited to mentor, coach and develop talent.
• Excellent written and verbal communication skills. You are able to articulate research ideas and outcomes to both technical and non-technical stakeholders.
• Exceptional communication and stakeholder management skills, with the ability to influence executives, customers, and cross-functional partners

Nice to Have

• Hands-on experience building and deploying agent-based, tool-augmented, or workflow-driven LLM systems in enterprise environments
• Prior ownership of enterprise AI platforms, internal ML products, or customer-facing AI services at scale
• Proven track record of partnering directly with enterprises to identify high-impact use cases and deliver measurable business outcomes

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, fueling the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re amplifying access to high-quality data to drive progress toward Artificial General Intelligence (AGI). Building on our history of model evaluation with enterprise and government customers, we are expanding our capabilities to set new standards for both public and private evaluations.

About This Role

This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). The ideal candidate will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale’s data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning. Success in this role requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling challenges related to data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions.

Ideally, you’d have:

• Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow. You should also be skilled at rapidly interpreting research literature and turning new ideas into working prototypes.

• A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.).

• At least three years of experience solving complex ML challenges, either in a research setting or product development, particularly in areas related to LLM capabilities and reasoning.

• Strong written and verbal communication skills, along with the ability to work effectively across teams.

Nice to have:

• Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX.

• Research and practical experience in building applications and evaluations related to LLM-based agents, including tool-use, text-to-SQL, browser agents, coding agents, and GUI agents.

• Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar.

• Familiarity with advanced agentic reasoning techniques such as STaR and PLANSEARCH.

• Proficiency in cloud-based ML development, with experience in AWS or GCP environments.

Our research interviews are designed to assess candidates' ability to prototype and debug ML models, their depth of understanding in research concepts, and their alignment with our organizational culture. We do not conduct LeetCode-style problem-solving assessments.

About the Role

The Director of Business Systems will be responsible for building real depth across Finance, GTM, Operations, and People systems, connecting these capabilities into a powerful, AI-integrated backbone.

You are a builder-first leader who will blend enterprise architecture with cutting-edge AI automation. Your goal is to eliminate fragmentation and enable every function to move faster by deploying internal AI agents that reduce manual work and surface actionable insights. You will lead the charge in making our internal operations as sophisticated as the external products we build.

Key Responsibilities

• Enterprise Application Strategy: Own the full lifecycle of our application ecosystem, including ERP (Finance), CRM (GTM), HRIS (People), and specialized AI tools. Ensure these systems converge into a secure, scalable backbone.
• AI Agent Deployment: Identify opportunities to replace manual workflows with AI/LLM-powered agents. Build and manage "internal agents" that automate forecasting, revenue capture, and employee self-service.
• Cross-Functional Partnership: Serve as the primary technology partner to the leadership team. Align system roadmaps with departmental goals to ensure seamless data flow from production to the back office.
• Operational Excellence: Mature our core systems by establishing disciplined change management, clear data governance, and measurable SLAs. Stabilize environments to support audit and regulatory requirements.
• Team Leadership: Lead a high-impact team that fosters a culture of curiosity, speed, and user-centricity.

Ideally, you have:

• 10+ years of experience leading business systems or enterprise applications in a fast-paced, high-growth environment.
• Technical Depth: Strong functional understanding of Salesforce (or equivalent CRM), NetSuite (or equivalent ERP), and Workday (or equivalent HRIS).
• AI/Automation Mindset: Proven track record of designing and deploying AI/LLM-powered workflows or agentic systems to improve business efficiency.
• Architecture Skills: Ability to design scalable data flows and APIs that link disparate SaaS tools into a cohesive ecosystem.
• Stakeholder Mastery: Experience influencing C-level executives and translating complex technical needs into business outcomes.
• Skilled in Python or SQL to personally audit or prototype automation logic.
• Extensive experience and In depth knowledge of the functionality of the ERP modules with emphasis on Sales – Order to Cash & Sales Audit, Purchasing – Direct & Indirect and Finance - General Ledger, AP & AR, Procure to Pay, Sales Audit, Fixed Assets, International Consolidations & Reporting
• Experience with GTM systems such as, HubSpot, Outreach, Clari, CPQ (e.g., Salesforce CPQ), and similar tools.

Scale Labs, Research Scientist — Frontier Risk Evaluations

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist focused on Frontier Risk Evaluations, you will design and create evaluation measures, harnesses and datasets for measuring the risks posed by frontier AI systems. For example, you might do any or all of the following: 

• Design and build harnesses to test AI models and systems (including agents) for dangerous capabilities such as security vulnerability exploitation, CBRN uplift, and other high-risk activities;
• Work with government agencies or other labs to collectively scope and design evaluations to measure and mitigate risks posed by advanced AI systems;
• Publish evaluation methodologies and write technical reports for policymakers.

Ideally you’d have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Practical experience conducting technical research collaboratively. You should be comfortable building and instrumenting ML pipelines, writing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Experience in crafting evaluations and benchmarks, or a background in data science roles related to LLM technologies.
• Experience with red-teaming or adversarial testing of AI systems.
• Familiarity with AI safety policy frameworks (e.g., NIST AI RMF, EU AI Act, Korea AI Basic Act).

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.

Senior Software Engineer, Platform — Connectivity

The Connectivity team owns the core platform that enables Scale to continuously generate, evaluate, and deliver high-quality training data to our customers. Our systems sit at the center of Scale’s data and AI infrastructure, powering large-scale workflows while supporting frontier AI capabilities such as multi-modality, long-horizon reasoning, and agentic behavior.

We’re looking for a senior engineer with deep experience building robust platforms, someone who brings strong architectural judgment, a bias toward correctness and reliability, and the ability to design systems that scale and evolve cleanly over time. This is a backend-heavy role with full-stack expectations and significant ownership from design through long-term operation.

What You’ll Do

• Design and own foundational platform systems that support scalable data generation, evaluation, and bespoke customer delivery across Scale’s ecosystem.
• Architect extensible, production-grade services that can support frontier AI workflows, including multi-modal inputs, long-running processes, and agentic orchestration.
• Build and operate distributed systems at scale, with strong guarantees around correctness, reliability, observability, and cost efficiency.
• Integrate with public LLM APIs and AI services, designing abstractions that are resilient to model churn, latency variability, and evolving usage patterns.
• Design and maintain data transformation and processing systems, supporting complex schema evolution, customization, and high-throughput workloads.
• Partner closely with infrastructure, product, and customer-facing teams to define requirements, shape technical direction, and deliver seamless integration experiences for customers.
• Lead multi-quarter technical initiatives, including authoring and driving a 6+ month technical roadmap for major platform investments.
• Apply strong engineering judgment in ambiguous problem spaces, balancing speed with long-term maintainability and operational excellence.
• Raise the quality bar through thoughtful system design reviews, rigorous code reviews, and mentorship grounded in real-world production experience.

Ideal Experience

• 7+ years of professional software engineering experience, with a strong background in building and operating large-scale, production-grade platforms.
  
  
• Deep expertise in distributed systems and cloud-native architectures, including Kubernetes, microservices, event-driven systems, caching, and production databases.
  
  
• Proven ability to lead multi-quarter technical initiatives, work effectively across cross-functional teams, and apply strong architectural judgment in ambiguous environments.
  
  
• Preferred: Experience integrating with public LLM APIs and designing systems that handle scale, reliability, latency, and cost tradeoffs.
  
  

Preferred: Experience building or operating data processing and transformation systems supporting complex workflows and evolving schemas.

Scale plays a critical role in powering the next generation of AI applications across the enterprise. As our customer base grows exponentially, Principal Solutions Engineers operate as senior technical leaders on the front lines—shaping how the world’s most innovative companies adopt, deploy, and scale AI with Scale.

Principal Solutions Engineers are trusted advisors to both customers and internal stakeholders, owning the most complex and high-impact opportunities. They partner closely with Sales, Product, and Engineering leadership to drive technical strategy, secure executive alignment, and deliver differentiated solutions that win and expand strategic accounts. Beyond individual deals, they influence product direction, define best practices, and elevate the capabilities of the broader Solutions Engineering organization.

You will:

• Own technical strategy and execution for the company’s most complex, high-value enterprise opportunities, acting as the ultimate arbiter of the “technical win”
• Serve as a trusted advisor to executive stakeholders (CTOs, Heads of AI, Engineering leaders), guiding long-term AI/LLM adoption strategies
• Architect and lead end-to-end solution design, from discovery through pilots and productionization, across ambiguous and evolving customer requirements
• Drive cross-functional alignment across Sales, Product, and Engineering to unblock critical technical and business challenges
• Define and shape large, multi-phase Scopes of Work, translating ambiguous customer problems into scalable, production-ready solutions
• Lead high-impact customer engagements, including executive presentations, technical deep dives, and workshops
• Influence Scale’s product roadmap by synthesizing customer insights and advocating for high-leverage feature development
• Mentor and elevate other Solutions Engineers, establishing best practices across demos, pilots, and technical sales strategy
• Drive org-level initiatives that improve win rates, shorten sales cycles, and increase the scalability of the Solutions Engineering function

Ideally, you'd have:

• Deep technical background with significant experience in customer-facing engineering roles (pre-sales, forward-deployed, or post-sales) at enterprise scale
• Proven track record of owning and closing complex, multi-stakeholder technical deals with Fortune 500 or equivalent enterprises
• Strong systems thinking and architecture experience across cloud, data, and AI/ML systems
• Hands-on experience with programming languages such as Python, Java, or similar, with the ability to prototype and validate solutions when needed
• Experience operating as a senior technical advisor to executive stakeholders, with strong presence and credibility
• Demonstrated ability to navigate ambiguity, influence without authority, and drive alignment across diverse teams
• Exceptional communication skills, with the ability to translate between business, product, and technical audiences
• High degree of ownership, intellectual curiosity, and bias for action in fast-paced, ambiguous environments

Nice to haves:

• Deep experience with Generative AI / LLM applications in enterprise environments
• Background in forward-deployed engineering or building production-grade ML systems
• Experience in regulated industries such as financial services, insurance, or healthcare

Scale plays a vital role in the development of AI applications. Our customer base is growing exponentially, and you will be on the front lines, ensuring that the world's most innovative companies become passionate, lifelong Scale customers.

Solutions Engineers partner closely with AEs, Product, and MLEs to lead prospective customers through pre-sales, delivering customized demos and pilots to secure the “technical win”. Solutions Engineers scope customer technical requirements and develop an actionable SOW. They will work closely with the delivery team to help with initial implementation. Solutions Engineers are relentlessly curious about customer needs and pain points. They employ their expert Scale product knowledge and GenAI knowledge to design solutions that best address these needs. Solutions Engineers are strong relationship builders, great project managers, and provide technical expertise.

You will:

• Partner with Scale AEs on the customer journey, delivering tailored demos and prototypes according to the customer's requirements.
• Develop technical domain expertise in Generative AI / large language model applications for Enterprise use cases, including customers in financial services, insurance, SaaS, and similar enterprises.
• Be accountable for securing the “technical win” by unblocking technical challenges
• Interact with customers daily to understand their needs and design solutions to better serve them.
• Design and develop “Scopes of Work” by breaking down customer challenges into a project plan
• Work closely with forward-deployed Software and Machine learning Engineers to develop agents in the initial post-sales stage
• Work with AEs and PMs to identify customer-specific feature requests.
• Drive strategic initiatives to improve the efficiency and effectiveness of the Solution Engineering team.

Ideally, you'd have:

• Strong engineering background with prior experience working with clients in a pre or post-sales capacity to realize business goals.
• Prior experience developing with Python, Java and/or other web development languages.
• Experience working in enterprise SaaS, cloud tech, finance, fintech or similar industries in a technical capacity with end-customer engagement.
• A track record as a self-starter, motivated to independently unblock technical issues in the field with the customer, away from the mothership.
• Presentation skills with a high degree of technical credibility when speaking with executives and front-line engineers.
• High level of comfort communicating effectively across internal and external organizations.
• Intellectual curiosity, empathy, and ability to operate with high velocity.

Nice to haves:

• GenAI Experience
• Forward deployed engineering experience
• Machine Learning Experience

Scale’s ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality.

Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale’s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation.

If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you!

You will:

• Build, profile and optimize our training and inference framework
• Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation
• Research and integrate state-of-the-art technologies to optimize our ML system

Ideally you’d have:

• Strong excitement about system optimization
• Experience with multi-node LLM training and inference
• Experience with developing large-scale distributed ML systems
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. 
• Strong written and verbal communication skills and the ability to operate in a cross functional team environment

Nice to haves:

• Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

Scale's LLM post-training platform team builds our internal distributed framework for large language model training. The platform powers MLEs, researchers, data scientists, and operators for fast and automatic training and evaluation of LLMs. It also serves as the underlying training framework for the data quality evaluation pipeline.

Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimizing the platform to enable our next generation LLM training, inference and data curation.

If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you!

You will:

• Build, profile and optimize our training and inference framework.
• Collaborate with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.
• Research and integrate state-of-the-art technologies to optimize our ML system.

Ideally you’d have:

• Passionate about system optimization
• Experience with multi-node LLM training and inference
• Experience with developing large-scale distributed ML systems
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. 
• Strong written and verbal communication skills to operate in a cross functional team environment.

Nice to haves:

• Demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

As a member of our Frontier Tech Consultant team, you will play a critical role in advancing cutting-edge AI innovations by conducting high-impact experiments and ensuring seamless execution at the highest quality standards. Your work will directly contribute to Scale AI’s growth, shaping the future of artificial intelligence.

In this role, you will be working on various types of projects, including but not limited to: research experiments, dataset generation, data quality improvements, and in-depth technical analysis. You will tackle complex, technical and operational challenges while collaborating closely with Scale’s ML research scientists and SPM team.

The ideal candidate is analytical, detail-oriented, and results-driven, with strong problem-solving abilities and excellent communication skills. We are looking for someone who thrives in a fast-paced environment, is proactive in overcoming challenges, and is committed to delivering exceptional outcomes. If you are eager to contribute to the forefront of AI innovation, we encourage you to apply.

You will be responsible for:

• Design and execute research experiments
• Build and evaluate frontier LLM datasets
• Develop training and testing material for frontier pipelines
• Improve quality of existing and new products

Ideally you’d have:

• Strong machine learning knowledge, either by being in the final years of a ML PhD career or having already graduated
• Strong writing and verbal communication skills
• An action-oriented mindset that balances creative problem solving with the scrappiness to ultimately deliver results
• Analytical, planning, and process improvement capability
• Experience working in a fast-paced, entrepreneurial environment
• Technical skills including familiarity with Python, GPU, AWS, API, LLM, ML, and SQL

Pay: $60-80/hr 

Commitment: This is a fully remote, US-based part-time (10-20 hours per week), on-going contract position staffed via HireArt. 

HireArt values diversity and is an Equal Opportunity Employer. We are interested in every qualified candidate who is eligible to work in the United States. Unfortunately, we are not able to sponsor visas, including CPT/OPT or employ corp-to-corp.

#LI-Onsite

The goal of a Senior Machine Learning Engineer at Scale is to leverage techniques in the fields of generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experience in production environments. Our machine learning engineers take advantage of robust internal infrastructure and  unique access to massive datasets to deliver improvements to our customers.

Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge. Our work spans multiple modalities, with a strong focus on both large language models and computer vision. On the LLM side, we are developing agentic systems that help solve complex operational and planning challenges for government partners. This includes building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We're also advancing research in areas like reinforcement learning for agentic LLMs, with successful deployment into real-world operational environments. On the computer vision front, we're training advanced models to increase labeling throughput and automate perception tasks. Our efforts include building large-scale fine-tuning pipelines, training models across multiple modalities, and developing generalizable vision foundation models to support a wide range of defense applications.

You will:

• Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers
• Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
• Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines
• Work with massive datasets to develop both generic models as well as fine tune models for specific products
• Build scalable machine learning infrastructure to automate and optimize our ML services
• Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations
• Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment
• Comfortable with light travel (approximately 10%) for customer interaction and team needs
• This role will require an active security clearance or the ability to obtain a security clearance

Ideally You’d Have:

• Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment
• Solid background in algorithms, data structures, and object-oriented programming
• Strong programing skills in Python, experience in Tensorflow or PyTorch

Nice to Haves:

• Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
• Experience working with cloud platforms (eg. AWS or GCP) and deploying machine learning models in cloud environments
• Experience with computer vision, generative AI models, large language models, or agentic systems
• Familiarity with ML evaluation frameworks and agentic model design

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations.

About the ACE team 

The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. 

About This Role

This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate these advancements into real-world, scalable solutions.

Ideally you’d have: 

• Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow. You should also be adept at interpreting research literature and quickly turning new ideas into prototypes.
• A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.)
• At least three years of experience addressing sophisticated ML problems, either in a research setting or product development.
• Strong written and verbal communication skills and the ability to operate cross-functionally. 

Nice to have:

• Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax. 
• Hands-on experience and publications in building applications and evaluations related to AI agents such as tool-use, text2SQL, browser agents, coding agents and GUI agents.
• Hands-on experience with agent frameworks such as OpenHands, Swarm, LangGraph, etc.
• Familiarity with agentic reasoning methods such as STaR and PLANSEARCH 
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions.

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.

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.

At Scale AI, we help leading enterprises turn AI from a promising capability into reliable systems that improve real workflows and deliver measurable business value. We are hiring a Senior Forward Deployed Data Scientist / Engineer to work directly with customers on ambiguous, high-impact problems at the intersection of data science, product development, and AI deployment.

This is not a traditional analytics role. On this team, data scientists do the core statistical and modeling work, but they also build real tools and products: evaluation explorers, operator workflows, decision-support systems, experimentation surfaces, and customer-specific AI/data applications that get used in production. In many cases, the data scientist builds the first usable version of the solution, proves value quickly, and helps drive it into a durable product or platform capability.

The right candidate is strong in first-principles problem solving, rigorous measurement, and technical execution. They know how to define metrics, design experiments, diagnose failures, and build systems that people actually use. They are also comfortable using modern AI-assisted development tools to prototype and iterate quickly without sacrificing reliability, observability, or judgment. Python and SQL matter in this role, but as execution fluency in service of building better products and making better decisions.

What you’ll do

• Partner directly with enterprise customers to understand workflows, operational pain points, constraints, and success criteria
• Turn ambiguous business and product problems into measurable solutions with clear metrics, technical designs, and deployment plans
• Design and build internal and customer-facing data products, including evaluation tools, workflow applications, decision-support systems, and thin product layers on top of data/ML systems
• Build end-to-end solutions across data ingestion, transformation, experimentation, statistical modeling, deployment, monitoring, and iteration
• Design evaluation frameworks, benchmarks, and feedback loops for ML/LLM systems, human-in-the-loop workflows, and model-assisted operations
• Apply rigorous statistical thinking to experimentation, causal inference, metric design, forecasting, segmentation, diagnostics, and performance measurement
• Use AI-assisted development workflows to accelerate prototyping and product iteration, while maintaining strong engineering discipline
• Diagnose failure modes across data quality, model behavior, retrieval, workflow design, and user experience, and drive fixes into production
• Act as the voice of the customer to Product, Engineering, and Data Science, using field learnings to shape roadmap and platform capabilities

What we’re looking for

• 5+ years of experience in data science, machine learning, quantitative engineering, or another highly analytical technical role
• Proven track record of shipping data, ML, or AI systems that delivered measurable business or product impact
• Exceptional ability to structure ambiguous problems, define the right success metrics, and translate them into executable technical plans
• Strong foundation in statistics, experimentation, causal reasoning, and measurement
• Experience building tools or products, not just analyses — for example internal workflow tools, evaluation systems, operator-facing products, experimentation platforms, or customer-specific applications
• Hands-on fluency in Python, SQL, and modern data/AI tooling; able to inspect data, prototype quickly, debug deeply, and productionize solutions that work
• Comfort using AI-assisted coding and development workflows to move from idea to usable product quickly
• Strong communication and stakeholder management skills; able to work effectively with customers, engineers, product teams, and executives
• High ownership and bias toward shipping in fast-moving environments with incomplete information

Preferred qualifications

• Experience in a forward deployed, solutions, consulting, or other client-facing technical role
• Experience designing evaluation frameworks for LLMs, retrieval systems, agentic workflows, or other AI-enabled products
• Experience with large-scale data processing and distributed systems such as Spark, Ray, or Airflow
• Experience with cloud infrastructure and modern data platforms such as AWS, GCP, Snowflake, or BigQuery
• Experience building lightweight applications, APIs, internal tools, or workflow software on top of data/ML systems
• Familiarity with marketplace experimentation, causal inference, forecasting, optimization, or advanced statistical modeling
• Strong product instinct and the judgment to know when the right answer is a model, an experiment, a tool, or a workflow redesign

What success looks like

Success in this role means taking a messy, high-stakes customer problem and turning it into a deployed system that is actually used. Sometimes that system is a model. Sometimes it is an evaluation framework. Sometimes it is an operator-facing tool or a lightweight data product that changes how decisions get made. In all cases, success is defined by measurable impact, rigorous evaluation, and reliable execution.