Paperless Parts provides the manufacturing industry with a SaaS platform that empowers the next generation of manufacturers to join the digital age. When manufacturers use our product, they free up valuable time for faster quoting and estimating, leading to increased profitability and reduced manual steps. From life-saving medical devices to critical components that unlock new depths of outer space, our customers produce parts that go into some of the world’s coolest and most impactful products. It is our mission to enable manufacturers with the technology they need to drive innovation forward.

We are a team of motivated and hardworking creators and doers. If you’re looking to have a direct impact on a Series B fast-scaling company that is revolutionizing an essential industry, read on and apply!

This is a full-time hybrid position based in Boston, MA.

This position requires activities that are subject to US Export Control Laws and require US Citizenship or Green Card Holder.

About the Role 

Step into the role of Principal Software Engineer and serve as a technical force multiplier for our engineering organization. In this high-leverage position, you will look beyond the immediate roadmap to define the architectural vision that will support Paperless Parts for years to come. You will bridge the gap between business strategy and technical execution, ensuring our systems are robust, scalable, and AI-ready.

This role is a powerful catalyst for change and innovation. You will be responsible for modernizing our engineering practices—integrating AI agents into the development lifecycle, driving the evolution from monolithic to a service architecture, and mentoring our engineers to become the next generation of leaders.

Who You Are

• Technical Visionary & Pragmatist: You have a proven track record of evolving complex architectures in scale-up environments. You balance the pursuit of "technical purity" with business reality, making hard trade-offs with incomplete information.
• Force Multiplier: You don't just write great code; you make the entire team better. You are passionate about mentorship and teaching others effective patterns for design, review, and agentic coding.
• AI Pioneer: You think strategically about how AI agents and workflows can fundamentally change how we build software. You believe that "what is good for humans is good for AI," championing standards that serve both.
• Business Partner: You understand that technical debt is a business risk. You can translate complex infrastructure needs into tangible business value (ROI, reliability, speed) to help leadership prioritize effectively.

Why You'll Love Working Here

• Architectural Authority: You will lead the design of our most critical systems, making high-stakes decisions on migrating to distributed architectures and defining our data strategy.
• Optimizing the AI-Native Workflow: You will have the freedom to operationalize AI at scale—implementing agents for ticket resolution, automated reviews, and documentation—driving significant improvements in modern engineering efficiency.
• High-Impact Work: Our software is used in mission-critical industries like aerospace and defense. The reliability and performance standards you set directly impact the production of life-saving and critical components.
• Culture of Excellence: You will help establish a culture of deep observability and operational maturity, leading the charge on incident management and blameless postmortems.

What You'll Do

• Architect & Evolve: Lead the technical strategy for migrating legacy monolithic patterns to scalable, service architectures. You will define the long-term vision for our backend systems, APIs, and cloud infrastructure.
• Operationalize AI Development: Drive the adoption and continuous improvement of AI-assisted workflows (using tools like Cursor and Claude Code). You will champion the strategic integration of AI agents to enhance autonomous task execution and leverage AI throughout the software life cycle.
• Drive Operational Maturity: Instill a strong focus on reliability and site operations across the team. You will lead initiatives to improve observability (logging, monitoring), define clear runbooks, and facilitate postmortems that turn incidents into structural improvements.
• Elevate Engineering Standards: Enforce "context-first" engineering. You will champion the creation of Architectural Decision Records (ADRs) and high-quality documentation, ensuring our codebase provides the explicit context needed for both human alignment and effective AI agent performance.
• Mentor & Lead: Act as a technical beacon, dedicating significant time (~20%) to pairing, design reviews, and mentoring engineers across different levels to elevate the team's collective skill set.
• Strategic Alignment: Collaborate with R&D leadership to prioritize technical initiatives. You will articulate the ROI of infrastructure investments and technical debt reduction in terms of customer reliability and business agility.

What You'll Bring

• 12+ years of experience in software engineering, with a strong background in architecting SaaS products at scale (start-up to scale-up transition experience preferred).
• Deep Architectural Expertise: Proven success decomposing monoliths and designing distributed systems, with strong judgment on build-vs-buy decisions and technology selection.
• Modern Stack Proficiency: Expert-level knowledge of modern web frameworks and databases, along with deep experience in AWS cloud-native patterns (containers, serverless).
• AI/LLM Fluency: Practical experience leveraging LLMs and AI agents in a production engineering workflow. You understand prompt engineering, context limits, and how to structure codebases for AI interpretability.
• Operational Rigor: A strong commitment to production reliability, with experience designing systems for observability and leading incident response processes.
• Communication Mastery: Exceptional ability to communicate complex technical concepts to non-technical stakeholders and influence decisions without relying on authority.

Why Join Us?

• Impactful Work: Your contributions will power industries critical to our society and economy, making a real difference.
• Continuous Learning: We invest in our people and offer opportunities to learn, grow, and advance their careers.
• Supportive Culture: Our team is our greatest asset, and we foster an environment where everyone can thrive.

If you’re a forward-thinking engineer eager to make a significant impact while working with a talented, driven team, we’d love to hear from you!

Salary range:  $212,000 - $286,000

The Role

Fusion is a global electronics distributor building a modern data platform on top of a 20 year old legacy system. Palantir Foundry is a core part of that stack. We use it to deliver unified views of our customers, products, and operations, and we are leaning hard into AIP and agent driven workflows to compress delivery cycles. We are looking for a senior engineer who can work hands on in Foundry: building pipelines, ontology models, agents, and applications alongside our external Palantir consultants today, and progressively taking ownership as the platform matures.

This is an execution focused role with real autonomy. You will design and build solutions, work directly with business stakeholders, and have a voice in how the platform evolves.

What You’ll Do

• Build and maintain data pipelines: Design, build, and optimize batch and streaming pipelines in Foundry (Pipeline Builder with AI assist, Code Repositories, Code Workbook) that ingest data from internal and external source systems.
• Develop ontology models: Create and maintain ontology objects, relationships, link types, functions, and actions that represent business domains and power downstream applications, agents, and AIP workflows.
• Build AI native experiences with AIP: Use AIP Logic to compose AI driven functions, AIP Agent Studio to stand up domain agents, and AIP Threads or AIP Assist to embed conversational interfaces into Workshop apps. Use AIP Evals to validate prompt and agent behavior before promotion to production.
• Leverage AIP Analyst and FDE AI for delivery acceleration: Use AIP Analyst for ad hoc analytical work directly against the ontology, and lean on FDE AI and Solution Designer to scaffold ontology objects, pipelines, and starter applications. Refine the AI generated output, harden it for production, and bring it under proper change control.
• Deliver Foundry applications: Build and iterate on Workshop, Quiver, and Contour applications that give sales, operations, and leadership actionable views of the business, including embedded agentic actions and write back through Ontology Actions.
• Work alongside external consultants: Collaborate closely with our Palantir implementation partners to understand existing designs, validate data flows, and progressively take on more of the day to day Foundry work.
• Connect Foundry to the broader data platform: As the data modernization team builds out medallion layer domain stores and DBT based pipelines on Azure, help align Foundry’s ontology and ingestion paths so the platform can transition from legacy sources to the new architecture cleanly.
• Integrate with external systems: Build and maintain OSDK based integrations and APIs that connect Foundry to internal services and third party platforms.
• Engage stakeholders directly: Gather requirements from business teams, demo solutions, train end users, and incorporate feedback into iterations.
• Ensure data quality and observability: Implement validation, lineage tracking, health checks, and monitoring within Foundry pipelines. Build automated data quality checks, including AIP assisted ones, to keep data accurate and reliable.
• Apply AI tooling to your own workflow: Use AIP and AI assisted development practices to accelerate pipeline work, code review, and documentation.

What You Bring

Data Engineering Foundations

These are the bedrock skills we expect every senior data engineer on this team to have, regardless of platform. Foundry is the surface; these are the muscles underneath.

• 7+ years building production data pipelines in modern stacks.
• Strong SQL, including window functions, CTEs, set operations, and query optimization on large datasets. You can read an execution plan and explain the cost.
• Solid grasp of dimensional modeling (Kimball, star schema), Data Vault, or equivalent. You can defend a modeling choice with reasoning, not dogma.
• Experience designing for slowly changing dimensions, idempotent loads, late arriving data, backfills, and incremental processing.
• Proficiency in Python, including PySpark and pandas for transformation work.
• Working knowledge of the Spark execution model: partitioning, shuffles, skew, broadcast joins, and tuning.
• Experience with ETL and ELT patterns, CDC pipelines, and workflow orchestration.
• Familiarity with modern transformation frameworks such as DBT, including tests, documentation, snapshots, and incremental materializations.
• Understanding of columnar formats (Parquet, Delta, Iceberg), table formats, and partitioning strategies.
• Practical experience implementing data quality frameworks, data contracts, and SLAs between producers and consumers.
• Comfort with Git workflows, code review, branching strategies, and CI/CD for data platforms.

Palantir Foundry and AIP

• 2+ years hands on Palantir Foundry experience with delivered work you can describe in detail.
• Strong knowledge of core Foundry capabilities: Ontology Manager, Ontology Functions and Actions, Pipeline Builder, Code Repositories, Code Workbook, Workshop, Builds, and platform security (branching, resource allocation, rollbacks).
• Experience with Foundry analytical and integration tools: Quiver, Contour, OSDK, and Marketplace products.
• Hands on experience with AIP for augmenting data workflows: AIP Logic, AIP Assist and Threads, and AIP assisted Pipeline Builder.
• Familiarity with newer AIP surfaces is a strong plus: AIP Agent Studio, AIP Analyst, FDE AI and Solution Designer, AIP Evals, and Automate.
• Understanding of how to evaluate, guardrail, and version AI driven functions and agents in a production context.

Cloud, Architecture, and Stakeholder Skills

• Proficiency with Azure data services, or equivalent depth in AWS or GCP with willingness to work in Azure.
• Solid understanding of data modeling across relational, analytical, and document paradigms.
• Ability to translate business requirements into technical designs and explain trade offs to non technical stakeholders.
• Self directed work style. You identify what needs doing and get it done without waiting for direction.

Nice to Have

• Production experience with DBT on Azure (Synapse, Fabric, or Databricks).
• Experience with medallion architecture (Bronze, Silver, Gold) or lakehouse patterns on Databricks, Snowflake, or equivalent.
• Experience evaluating LLM powered features: prompt design, eval harnesses, hallucination guardrails, and cost or latency tuning.
• Experience building or operating real time pricing, recommendation, or scoring engines.
• Familiarity with electronics distribution, supply chain, or ERP data, particularly Dynamics GP and Great Plains.
• Background in domain driven design or microservices architecture.
• TypeScript or C# for OSDK consumers and downstream services.
• Exposure to CRM, payment, or logistics API integrations.

Why Fusion

• Greenfield meets legacy: You’ll help build a modern data platform while keeping a 20 year business running. Real engineering problems, not toy projects.
• Real impact, small team: A lean team where your work is visible and your judgment matters. No layers of approval.
• AI first engineering culture: We use AIP and AI tooling aggressively. One engineer with the right tools does the work of three.
• Global scale: Fusion operates across the US, Singapore, and Hong Kong. The platform serves users worldwide.

ABOUT THE TEAM:

ArsenalOS is the digital backbone of Anduril’s hardware enterprise. It connects the full lifecycle, from concept to build to field feedback, into one operating environment built for speed, traceability, autonomy, and continuous improvement. That environment includes not just the software used to design, plan, and execute work, but also the factory-facing infrastructure that connects operators, machines, test systems, and OT networks into the same adaptive Build Chain. It is not a support function, but rather a strategic investment by Anduril that the operating system of the hardware lifecycle is how we win. It will be one of the mechanisms by which Anduril out-builds, out-adapts, and out-scales the traditional defense industrial base.

ABOUT THE JOB:

We are looking for a Staff Software Engineer to join our QualityOS team within Forge MES (Manufacturing Execution System). As a Staff Engineer, you will set the technical direction and architectural standards. You'll architect and implement mission-critical distributed systems that scale Anduril's manufacturing operations while maintaining the technical bar for reliability, performance, and maintainability.

Your impact will span beyond the immediate team. You'll serve as a technical bridge between the QualityOS product team and our Platform/Foundations organization, ensuring we build on solid infrastructure while influencing platform capabilities to meet manufacturing needs. You'll represent the quality domain in broader MES architectural decisions, contributing to technical strategy that affects multiple product teams.

We're looking for someone who combines deep technical expertise with strong collaborative instincts—someone who can dive into complex distributed systems, establish patterns that others follow, and drive consensus across engineering teams. You'll balance hands-on technical execution on critical features with raising the technical standards of the team through architecture reviews, code quality, and operational excellence. Your decisions will have lasting impact on how Anduril scales manufacturing quality systems into a next-generation defense company.

WHAT YOU'LL DO

Technical Architecture & Execution:

• Define and drive technical architecture for QualityOS - make decisions on system design, scalability patterns, integration strategies, and platform vs. build tradeoffs that shape team execution
• Architect and implement complex distributed systems - build mission-critical services, APIs, and tooling that enable safe, fast delivery across manufacturing quality workflows
• Lead by example with engineering excellence - establish patterns through hands-on work: comprehensive testing, deployment safety (blue/green/canary), infrastructure as code, observability, and operational rigor

Cross-Team Influence & Collaboration:

• Bridge product and platform teams - collaborate with Platform/Foundations to shape shared infrastructure based on QualityOS needs while driving adoption of platform capabilities
• Partner across MES product teams - integrate QualityOS into cohesive manufacturing experiences, evolve data models across quality/production domains, and drive technical consensus on cross-cutting concerns
• Represent quality domain technically - work with architecture, security, operations, and infrastructure teams to ensure quality requirements influence broader technical decisions

Team & Technical Standards:

• Raise the technical bar for the team - through rigorous code review, architecture discussions, establishing shared ownership models, and improving practices that balance velocity with correctness
• Remove friction and unblock execution - identify systemic technical issues, eliminate single points of failure, and create leverage through automation and improved tooling

REQUIRED QUALIFICATIONS

• 12+ years of experience in a software engineering role building products in fast-paced environments
• Proven ability to influence technical direction across teams - drive architectural consensus, build alignment, and lead technical initiatives beyond a single team
• Experience working with large enterprise applications and platforms with high availability requirements
• Strong full-stack capabilities with expertise in modern web technologies - JavaScript/TypeScript, React, and web frameworks (Next.js, Remix, etc.)
• User-focused engineering mindset - ability to translate user needs into technical solutions while balancing user experience with engineering constraints
• Excellent communication skills, able to influence stakeholders, translate ambiguous requirements and build cross-team alignment.
• U.S. Person status is required as this position needs to access export controlled data

PREFERRED QUALIFICATIONS

• We encourage people from all backgrounds to apply but our tech stack uses AWS, Typescript, and React/Remix so familiarity with all of these tools will be particularly helpful
• Experience in hyper growth startup-like environments. Demonstrated successes in scaling and maturing software development processes, team culture and methods, and ability to balance short-term and long-term trade offs
• Manufacturing/quality domain knowledge - experience with quality management systems (QMS), manufacturing execution systems (MES), or incident management platforms
• Experience in defense, autonomy, robotics, or mission-critical systems (helpful but not required)
• Experience with AI/LLM integration - leveraging state-of-the-art models and agentic tooling in production software development
• Eligible to obtain and maintain a U.S. TS/SCI clearance

Strategic Account Executive at Unframe 

Location: Boston, MA (Remote)

About Unframe

Unframe is an AI-first startup helping the world’s largest enterprises bring LLM-powered applications to life in days - not months. We combine the speed of a product company with the flexibility of a consultancy, helping customers move from idea to deployed AI systems faster than anyone else in the market.

Backed by Bessemer, Craft, and TLV Partners with $50M in Series A funding, we’re building a fast-growing, revenue-generating company working with Fortune 500 customers globally

About the role:

We’re looking for Strategic AEs to join our team and help drive our go-to-market motion. You’ll play a pivotal role in landing our first major customers, shaping our sales playbook, and partnering directly with the VP of Sales and founder to build a high-performance revenue engine.

What you’ll do:

• Lead complex, multi-stakeholder sales cycles into highly regulated industries (FinServ, Healthcare, Manufacturing, Retail) where our on-premises capabilities provide a competitive advantage.
• Build pipeline from zero in an early-stage environment—identify and win strategic enterprise accounts with no hand-raisers or established playbook.
• Translate complex AI/ML capabilities into compelling business outcomes for C-suite stakeholders, positioning Unframe for companies seeking to “buy vs. build” AI capabilities.
• Architect multi-year expansion strategies and build deep relationships across IT, product, and innovation teams.
• Collaborate directly with the VP of Sales and founders to refine the enterprise strategy and influence the product roadmap

What we are looking for:

• 8+ years enterprise software sales with consistent quota over-achievement at Fortune 500 companies
• Proven track record closing $1M+ ACV deals in early-stage environments
• Experience selling complex technical solutions to CIO organizations
• Background in AI/ML platforms, digital transformation, enterprise data platforms, or management consulting
• Entrepreneurial mindset, you thrive in ambiguity, own outcomes, and build from scratch
• Success in highly regulated industries with complex compliance requirements

Nice to have:

• Regulated industry experience, early-stage success

Why join us?

• Compensation is uncapped with aggressive accelerators. At $1M+ ACV deal sizes, this structure rewards over-performance on meaningful deals.
• Our VP of Sales joined Unframe for three reasons: a founder with a proven exit, tier-one investors, and a product that's genuinely different and defensible. The GTM approach backs that up. Buyers get a free POC, tailored to their actual use case, delivered in days. No data sharing, no upfront cost, no six-month consulting engagement before anyone sees value.
• You'll work directly with the VP of Sales and founders, shape GTM strategy, close the deals that define our market position, and hold equity in a company with real institutional backing and momentum.
• We hire with intention. If you thrive in a performance culture with real backing behind you, you will win here.

Ready to Build the Future?

If this opportunity excites you, apply now!

Layer Health was founded in 2023 by leading machine learning researchers from MIT and Harvard Medical School. We are building an AI layer that can accurately and scalably synthesize information from medical records, with the mission to reduce friction everywhere in healthcare. Our LLM-powered platform is solving chart review once and for all, across use cases. For health systems, our first product dramatically accelerates clinical registry abstraction in areas ranging from surgery and cardiology, to oncology. Our long term vision is for our AI layer to safely transform patient care and minimize unnecessary heartbreak. Layer Health’s diverse founding team brings expertise across machine learning, UI/UX, large language models, and medicine.

Here’s a collection of articles about our product, mission, recent funding round, etc.

Job Description

You will work with the Layer Health ML team applying our existing ML workflow to tailor models for a specific clinical use case. You will design, build, evaluate, and iterate on these models — combining strong data science fundamentals with modern LLM techniques to deliver high-accuracy, production-ready solutions across new clinical domains.

This is a high-velocity, high-ownership role for someone who enjoys diving deep into error modes, making targeted improvements, and closing the loop between model performance and real-world impact.

This contract will require full-time hours.

What you’ll do:

• Own end-to-end development of models for specific use cases: Build and iterate using our ML/LLM-powered workflows for new clinical areas, from initial prototyping through production refinement.
• Conduct evaluation & error analysis: Own rigorous evaluation of model performance; perform deep error analysis to identify systematic failure modes and drive targeted improvements.
• Ship frequent, high-quality updates to models based on data, feedback, and observed edge cases.
• Leverage LLM application & tooling: Apply modern LLM techniques (prompting strategies, structured outputs, tool use, eval frameworks) to improve accuracy and robustness.
• Support analytics & metrics: Contribute to metrics, reporting, and internal dashboards that track model performance and downstream impact.

We look for:

• Good data science fundamentals, including knowledge of best practices for evaluating model performance and error analysis. (A proven track record of successful projects in healthcare or clinical applications is a bonus, but not required.)
• Familiarity with modern applied LLM techniques and their practical implementations, for example e.g. standard prompting techniques, LLM frameworks, structured output usage, etc. (any experience using these techniques is a bonus).
• Solid programming skills in Python.
• A strong technical communicator who thrives in a fast-paced environment.
• Proactive mindset to identify and solve problems, continuously improving our data science capabilities.
• An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML, alongside an awesome team. 
• Willingness to be hybrid with our team in either our Boston or NYC office 3 days per week.

Expected compensation range for this role is $40 - $70/hour. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made. 

If you are excited about this role, we encourage you to apply even if you don't feel that you meet every single requirement. We're eager to meet people that believe in our mission and can contribute to our team in a variety of ways. We welcome diverse perspectives, rigorous thinking, and fearlessness in challenging the status quo. 

Join us and help us transform healthcare with AI. Layer Health is committed to foster an environment of inclusion that is free from discrimination.  We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. 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 other characteristic protected by law. 

What we do  

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

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

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

What you’ll do: 

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

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

Who you are: 

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

#LI-TP1

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

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

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

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

#LI-TP1

About the role:

As a Product Support Specialist, you’ll be at the front lines of safely delivering AI to the world by responding to, investigating, and tracking user needs in your day to day. Additionally, you’ll help us identify – and close – gaps in our team’s technical knowledge, provide high-touch support to strategic customers, and demonstrate deep care for how we systematically support customers at scale. 

Responsibilities:

• Become an expert in all Anthropic products
• Respond to user support cases with a variety of complexity, from questions for individuals to complex API debugging for large businesses 
• Clearly and empathetically communicate with a wide range of user personas, context-switching between guiding executives in a high-touch model to assisting consumer users in a rapid pace
• Manage on-call tasks for high-urgency user issues with extreme ownership
• Prioritize critically and comfortably adapt to an ever-evolving product landscape
• Operate in ambiguity, making informed decisions even in never-before-seen situations
• Partner with engineers, teammates, and other internal stakeholders to diagnose and resolve user issues, both individually and at scale
• Suggest and drive improvements to increase user satisfaction through support processes as well as own initiatives that increase efficiency and drive down contact rates
• Uplevel our team’s technical knowledge by scoping gaps, working with cross-functional partners to deeply understand relevant nuances, and building resources that grow with our products

You may be a good fit if you:

• 3 - 5 years of relevant experience in technical product support in a high growth tech company, including API debugging, preferably in a second tier, escalated, or priority support team
• Are familiar with APIs and technical SaaS products and can deeply understand technical docs with ease
• Have demonstrated an ability to thrive in fast-paced, reactive situations while meeting core support metrics targets (e.g. CSAT, SLA, etc.)
• Possess strong user empathy and are expert in the lifecycle of a support case; you can read between the lines of a user’s question, put yourself in their shoes, and get at the heart of their needs for a speedy, satisfying resolution
• Have crisp but kind written communication skills and a deep care for the details
• Enjoy helping others learn about new features and complex concepts
• Experience troubleshooting SSO, SAML, and OAuth authentication flows
• Are persistent and curious; you delight in the hunt of tracking down a bug or issue, and are energized by fixing this for all similar users going forward
• Have experience contributing to the foundations of a support team – this is essential, highly valuable, but often unglamorous work
• Are proficient at working in a technical environment and are interested in Anthropic’s products
• Possess a deep sense of ownership, and are excited to help us build our team!

Strong candidates may also have:

While not required, we're particularly excited about candidates with one or more of these specializations:

• Comfort with command line interfaces and basic scripting (Bash, Python, JavaScript)
• Understanding of LLM capabilities, practical applications, and current limitations
• Familiarity with enterprise networking concepts and IT infrastructure
• Familiarity with Git workflows and version control concepts
• SQL proficiency for querying logs and investigating issues
• Experience supporting government or public sector customers, including familiarity with compliance requirements and security frameworks
• Background in team lead roles or managing contract/vendor support teams

We're hiring across a range of experience levels to build depth in both technical capabilities and enterprise support.

We are seeking a AI Chief Engineering Lead to drive innovations in autonomous vehicle technology using deep learning and reinforcement learning. In this dynamic role, you will design state-of-the-art algorithms and systems that enable safe, efficient, and intelligent autonomous capabilities. Today, employing mass quantities of autonomous robots requires heavy human oversight and execution. Anduril is leveraging AI approaches to improve effectiveness of autonomous missions, offload operator burden, and speed up execution via realtime monitoring, recommendations to users, and multi-modal interaction patterns. You will apply proven and un-proven approaches to create prototypes for expanding the capability of autonomous systems.

What You’ll Do

• Develop Advanced Agentic Software - Design and implement novel agent-based software systems to improve sensor perception, prediction, and decision-making for autonomous vehicles
• Apply Agentic Reasoning - Design and implement integrated agents and AI models to solve for end-user autonomous systems workflows.
• End-to-End System Integration - Collaborate with cross-functional teams to integrate research prototypes into robust, production-ready systems including simulation environments and real-world platforms.
• Research & Experimentation - Conduct research into reinforcement learning strategies and deep architectures, iterate on experimental designs, and evaluate performance using rigorous quantitative metrics.
• Data-Driven Innovation - Utilize real-world and synthetic data to enhance model robustness and generalization, leveraging scalable training pipelines on distributed systems.

Required Qualifications

• Sophisticated knowledge of LLM's with an understanding of how they work and how they're applied
• Solid experience with reinforcement learning methods and their application to autonomous systems.
• Proven experience of shipping products end to end
• Experience with simulation or real-world validation for autonomous vehicles is highly desirable.
• A degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
• Eligible to obtain and maintain an active U.S. Top Secret security clearance
• Travel up to 40+% of time to build, test, and deploy capabilities in the real world

Preferred Qualifications

• PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
• Novel application track record and experience including first author publications, participation in peer reviewed conferences, contribution to open source projects, and demonstrated contribution to the ML and AI community.
• Proven experience in deep learning research and development, particularly in generative AI. This includes diffusion models and autoregressive generative models.
• Experience in multi-modal sensor data processing (e.g., cameras, LiDAR, radar).
• Familiarity with ML Ops best practices, including model versioning and reproducible research pipelines.
• Strong programming skills in Python and familiarity with C/C++ is a plus.

We are seeking a Generative AI Chief Engineer Lead to drive innovations in autonomous vehicle technology using deep learning and reinforcement learning. In this dynamic role, you will design state-of-the-art algorithms and systems that enable safe, efficient, and intelligent autonomous capabilities. Today, employing mass quantities of autonomous robots requires heavy human oversight and execution. Anduril is leveraging AI approaches to improve effectiveness of autonomous missions, offload operator burden, and speed up execution via realtime monitoring, recommendations to users, and multi-modal interaction patterns. You will apply proven and un-proven approaches to create prototypes for expanding the capability of autonomous systems.

What You’ll Do

• Develop Advanced Agentic Software - Design and implement novel agent-based software systems to improve sensor perception, prediction, and decision-making for autonomous vehicles
• Apply Agentic Reasoning - Design and implement integrated agents and AI models to solve for end-user autonomous systems workflows.
• End-to-End System Integration - Collaborate with cross-functional teams to integrate research prototypes into robust, production-ready systems including simulation environments and real-world platforms.
• Research & Experimentation - Conduct research into reinforcement learning strategies and deep architectures, iterate on experimental designs, and evaluate performance using rigorous quantitative metrics.
• Data-Driven Innovation - Utilize real-world and synthetic data to enhance model robustness and generalization, leveraging scalable training pipelines on distributed systems.

Required Qualifications

• Sophisticated knowledge of LLM's with an understanding of how they work and how they're applied
• Solid experience with reinforcement learning methods and their application to autonomous systems.
• Proven experience of shipping products end to end
• Experience with simulation or real-world validation for autonomous vehicles is highly desirable.
• A degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
• Eligible to obtain and maintain an active U.S. Top Secret security clearance
• Travel up to 40+% of time to build, test, and deploy capabilities in the real world

Preferred Qualifications

• PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
• Novel application track record and experience including first author publications, participation in peer reviewed conferences, contribution to open source projects, and demonstrated contribution to the ML and AI community.
• Proven experience in deep learning research and development, particularly in generative AI. This includes diffusion models and autoregressive generative models.
• Experience in multi-modal sensor data processing (e.g., cameras, LiDAR, radar).
• Familiarity with ML Ops best practices, including model versioning and reproducible research pipelines.
• Strong programming skills in Python and familiarity with C/C++ is a plus.
• General software engineering experience solving motion planning or related robotics problems.

ABOUT THE TEAM:

The Developer Platform team builds the tools and infrastructure that power software development at Anduril. We own the company's primary developer CLI (2,300+ engineers, 93k+ invocations), CI/CD pipelines serving 3,500+ engineers across 23 product lines in the US, Australia, and UK, deployment and release management systems, and Anduril's LLM adoption platform. We are standing up a new agentic development initiative to build the next generation of AI-powered developer infrastructure. The team is growing fast and this is a ground-floor opportunity to define what agentic software development looks like at a company building mission-critical defense systems.

WHAT YOU'LL DO:

• Build multi-agent orchestration infrastructure that moves AI coding agents from individual developer machines to servers executing work autonomously across repositories at company scale
• Design and implement an agent-optimized, real-time knowledge base that gives AI agents deep context on codebases, architecture, and operational patterns
• Build a skills and plugin platform that lets any team encode their domain knowledge into reusable AI capabilities
• Improve CI/CD reliability and developer experience across 23 product lines
• Create release tooling that simplifies shipping software to production environments and robotic platforms
• Operate and evolve Anduril's LLM adoption platform: model rollouts, capacity management, cost attribution, and developer onboarding

REQUIRED QUALIFICATIONS:

• Strong software engineering fundamentals in one or more of: Go, Python, TypeScript
• An entrepreneur, builder, PM, and designer rolled into one. We need people with outstanding software taste who can identify the right problem, design the solution, build it, and ship it in days, not quarters.
• A passion for developer tooling. You notice friction in your own workflow and cannot help but fix it. You spend 4 hours sharpening the axe and 30 minutes chopping wood.
• Demonstrated ability to quickly learn and adapt to unfamiliar technologies across multiple domains
• Must be a U.S. Person due to required access to U.S. export controlled information or facilities

PREFERRED QUALIFICATIONS:

• Experience building developer tools, CLIs, CI/CD systems, or platform infrastructure
• Experience with Kubernetes, Nix, or cloud infrastructure (AWS/Azure)
• Genuine interest in LLMs and agentic systems as applied to developer productivity. If you are already building with AI coding agents in your daily workflow, you will feel right at home.
• Prior work on LLM integrations, prompt engineering, or agent frameworks

Mission Summary:

As a Staff Engineer within the Platform Engineering team, you will be a hands-on technical leader driving the evolution of Motional's global cloud and hybrid infrastructure. Operating as a senior individual contributor, you'll tackle our hardest platform problems directly — designing systems, creating code, and setting technical direction for initiatives that span multiple teams. Your work will help transform our R&D environment into a hardened, cost-efficient, and scalable commercial backbone, and ensure our internal platforms deliver the developer velocity required for rapid AV deployment.

What you'll be doing:

• Design and build major components of our cloud architecture — including networking, storage, compute abstraction layers. You'll own the technical design end-to-end and create code where it matters most
• Drive technical evolution of our Kubernetes-based developer platform. Identify the highest-leverage improvements to scalability, cost attribution, access control, and developer ergonomics — then design and implement them to reduce configuration complexity and accelerate engineering velocity
• Raise the reliability bar across Platform Engineering. Define and implement standards for SLIs, SLOs, observability, and failover, and partner with peer teams to roll them out across platform-layer services
• Drive FinOps practices — cost visibility, attribution, accountability as well as optimization recommendations
• Design and ship AI-native developer experiences by integrating LLM-powered tooling into CI, build systems, monitoring platforms, and local development workflows to remove friction and boost engineering throughput
  
  

What we're looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 9 years in Software or Infrastructure Engineering, with significant experience in a senior IC capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (Operators (Crossplane, ArgoWorkflow/Airflow, KubeRay), Kubecost/OpenCost, Service Mesh, GitOps and other CNCF projects)
• Proven track record of designing or migrating complex workloads between on-prem data centers and major cloud providers (AWS, GCP, other)
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff
  
  

Bonus points (not required):

• Proven expertise in CI/CD orchestration, build systems (Bazel, RBE), frameworks
• Prior experience with large-scale machine learning training pipelines
• Experience leading technical projects in a globally distributed team
• Open-source contributions to Kubernetes, observability, or infrastructure tooling ecosystems

Mission Summary:

As a Staff Engineer within the Platform Engineering team, you will be a hands-on technical leader driving the evolution of Motional's global cloud and hybrid infrastructure. Operating as a senior individual contributor, you'll tackle our hardest platform problems directly — designing systems, creating code, and setting technical direction for initiatives that span multiple teams. Your work will help transform our R&D environment into a hardened, cost-efficient, and scalable commercial backbone, and ensure our internal platforms deliver the developer velocity required for rapid AV deployment.

What you'll be doing:

• Design and build major components of our cloud architecture — including networking, storage, compute abstraction layers. You'll own the technical design end-to-end and create code where it matters most
• Drive technical evolution of our Kubernetes-based developer platform. Identify the highest-leverage improvements to scalability, cost attribution, access control, and developer ergonomics — then design and implement them to reduce configuration complexity and accelerate engineering velocity
• Raise the reliability bar across Platform Engineering. Define and implement standards for SLIs, SLOs, observability, and failover, and partner with peer teams to roll them out across platform-layer services
• Drive FinOps practices — cost visibility, attribution, accountability as well as optimization recommendations
• Design and ship AI-native developer experiences by integrating LLM-powered tooling into CI, build systems, monitoring platforms, and local development workflows to remove friction and boost engineering throughput
  
  

What we're looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 9 years in Software or Infrastructure Engineering, with significant experience in a senior IC capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (Operators (Crossplane, ArgoWorkflow/Airflow, KubeRay), Kubecost/OpenCost, Service Mesh, GitOps and other CNCF projects)
• Proven track record of designing or migrating complex workloads between on-prem data centers and major cloud providers (AWS, GCP, other)
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff
  
  

Bonus points (not required):

• Proven expertise in CI/CD orchestration, build systems (Bazel, RBE), frameworks
• Prior experience with large-scale machine learning training pipelines
• Experience leading technical projects in a globally distributed team
• Open-source contributions to Kubernetes, observability, or infrastructure tooling ecosystems

Mission Summary:

As a Staff Engineer within the Platform Engineering team, you will be a hands-on technical leader driving the evolution of Motional's global cloud and hybrid infrastructure. Operating as a senior individual contributor, you'll tackle our hardest platform problems directly — designing systems, creating code, and setting technical direction for initiatives that span multiple teams. Your work will help transform our R&D environment into a hardened, cost-efficient, and scalable commercial backbone, and ensure our internal platforms deliver the developer velocity required for rapid AV deployment.

What you'll be doing:

• Design and build major components of our cloud architecture — including networking, storage, compute abstraction layers. You'll own the technical design end-to-end and create code where it matters most
• Drive technical evolution of our Kubernetes-based developer platform. Identify the highest-leverage improvements to scalability, cost attribution, access control, and developer ergonomics — then design and implement them to reduce configuration complexity and accelerate engineering velocity
• Raise the reliability bar across Platform Engineering. Define and implement standards for SLIs, SLOs, observability, and failover, and partner with peer teams to roll them out across platform-layer services
• Drive FinOps practices — cost visibility, attribution, accountability as well as optimization recommendations
• Design and ship AI-native developer experiences by integrating LLM-powered tooling into CI, build systems, monitoring platforms, and local development workflows to remove friction and boost engineering throughput
  
  

What we're looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 9 years in Software or Infrastructure Engineering, with significant experience in a senior IC capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (Operators (Crossplane, ArgoWorkflow/Airflow, KubeRay), Kubecost/OpenCost, Service Mesh, GitOps and other CNCF projects)
• Proven track record of designing or migrating complex workloads between on-prem data centers and major cloud providers (AWS, GCP, other)
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff
  
  

Bonus points (not required):

• Proven expertise in CI/CD orchestration, build systems (Bazel, RBE), frameworks
• Prior experience with large-scale machine learning training pipelines
• Experience leading technical projects in a globally distributed team
• Open-source contributions to Kubernetes, observability, or infrastructure tooling ecosystems

Position Summary

We are seeking a Quantitative Analyst with three or more years of experience to help advance our research and investment analytics capabilities, with a particular emphasis on data science. This highly capable person will build and maintain a thoughtful data layer across portfolios, managers, and asset classes, while developing analytical tools and workflows that improve decision-making and efficiency. This is a hybrid role that blends investment domain knowledge with data science and applied AI, offering an opportunity to help shape the evolution of our investment platform.

This role is located in Boston, MA.

Primary Responsibilities

• Partner with senior investment professionals and other colleagues to translate strategic priorities into practical analytical solutions, workflow enhancements, and automation opportunities.
• Support the Investment team in monitoring existing investments and underwriting new opportunities, including internal discussions related to portfolio construction, performance, and risk.
• Build and maintain scalable data pipelines and workflows to support reliable data ingestion, transformation, and quality across investment, portfolio, and client datasets.
• Help develop and enhance the firm’s investment data layer, with a focus on making information more structured, accessible, and actionable across asset classes and investment processes.
• Analyze large and complex datasets to identify trends, behavioral patterns, performance drivers, and other insights that can inform investment decision-making.
• Design, validate, and operationalize analytical models and AI-enabled tools to support areas such as asset allocation, manager research, portfolio monitoring, and risk assessment.
• Maintain strong documentation and adhere to appropriate data governance, control, and compliance standards consistent with industry best practices.

Qualifications

Required

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Finance, or a quantitative field.
• Three or more years of relevant experience in data science, analytics, investment research, or a related role, ideally within financial services, asset management, or wealth management.
• Experience using Git and GitHub for version control, collaborative development, code reviews, and managing production-grade analytical or quantitative applications.
• Strong programming skills with experience building analytical workflows, models, and data pipelines.
• Experience working with modern data infrastructure, including Snowflake or similar environments.
• Solid understanding of financial markets, portfolio construction, investment performance, and risk concepts.
• Excellent communication skills and the ability to explain complex analyses and technical concepts clearly.

Preferred

• Experience with business intelligence and data visualization platforms such as Power BI.
• Familiarity with cloud environments such as AWS, Azure, or GCP.
• Experience applying machine learning or AI tools in production or business workflows.
• Experience with agentic workflows using tools such as Copilot Studio or Claude.
• Familiarity with modern LLM ecosystems and related concepts such as retrieval-augmented generation (RAG), vector databases, agent frameworks, or tool/function calling.
• Exposure to investment manager research, underwriting, or portfolio analytics is a plus.

This position is an exempt position. The annualized base pay range for this role is expected to be between $150,000–$250,000 base salary compensation range. Actual base pay may vary based on factors including, but not limited to, experience, subject matter expertise, geographic location where work will be performed, and the applicant’s skill set. The base pay is just one component of the total compensation package. Other rewards may include an annual cash bonus and a comprehensive benefits package, including but not limited to medical, dental, vision, life insurance, and 401(k). Please note that the job title is subject to change based on the selected candidate’s experience and education.

SCS is a fiduciary wealth and investment management firm serving some of the world’s most ambitious wealth creators — ultra‑high‑net‑worth individuals, families, and institutions. SCS exists to transform wealth into possibility, helping its clients protect and amplify their capital, align it with their purpose, and build multi‑generational legacies.

SCS delivers an integrated family office and investment platform that brings together sophisticated multi‑asset class portfolio management, access to private investments, holistic wealth and estate planning, next‑generation education, and purpose‑driven advisory. SCS’s team is supported by a modern, scalable technology infrastructure that enhances SCS’s ability to deliver a seamless, proactive, white‑glove client experience.

SCS is part of Focus Financial Partners, a leading financial services firm comprised of wealth management, family office, and business management services.

#LI-SF1

Position Overview:

As the Director of Monetization Strategy, you will be the primary architect of Iterable’s economic engine. You aren't just managing a price list; you are defining how we capture value in a rapidly evolving MarTech ecosystem. You will sit at the intersection of Product, Sales, Finance, and Marketing to design and execute pricing and packaging strategies that drive sustainable growth, expand Net Revenue Retention (NRR), and align our success with our customers' success.

Responsibilities:

• • Strategic Vision: Lead the long-term monetization roadmap. You’ll evolve our current models from usage-based pricing toward value-based frameworks that reflect the power of our AI-driven orchestration.
  • Cross-Functional Leadership: Act as the "connective tissue" between Product (to price new features, and ensure our roadmap is optimized for capturing value), Marketing (to position value), and Sales (to ensure deal velocity).
  • Market Intelligence: Conduct deep-dive competitive analysis and buyer research to identify gaps in our current monetization strategy and capitalize on emerging market trends.
  • Packaging & Tiering: Design and optimize product bundles and editions that simplify the buying process for mid-market and enterprise customers alike.
  • Operational Excellence: Partner with RevOps to implement pricing changes within our systems (CPQ, billing) and develop the discounting frameworks and "guardrails" for the field.
  • Change Management: Lead the internal enablement for global sales teams, ensuring they can confidently articulate the "why" behind our pricing.

Qualifications:

• You have 10+ years of experience in SaaS monetization, management consulting, or strategic finance. You think in systems and understand the downstream impacts of a single pricing lever.
• You have direct experience in SaaS monetization, with a particular focus on monetizing AI
• You are comfortable navigating complex datasets to find the "signal" in the noise. You use data to prove your hypotheses about price elasticity and expansion paths.
• You can distill complex economic theories into simple, persuasive narratives for the Executive Leadership Team (ELT).
• You don't just want a seat at the table; you want to build the table. You enjoy creating processes where none existed.

Bonus Points

• Experience building and productionizing LLM based, agentic or autonomous systems that handle complex orchestration and evaluation.
• Experience establishing shared AI platforms or internal developer enablement capabilities
• Familiarity with analytics systems, experimentation workflows, or decisioning frameworks

Perks & Benefits:

• Paid parental leave
• Competitive salaries, meaningful equity, & 401(k) plan
• Medical, dental, vision, & life insurance
• Balance Days (additional paid holidays)
• Fertility & Adoption Assistance
• Paid Sabbatical
• Flexible PTO
• Monthly Employee Wellness allowance 
• Monthly Professional Development allowance 
• Pre-tax commuter benefits
• Complete laptop workstation

The US base salary range for this position at the start of employment is $172,000 - $260,000. Within this range, individual pay is determined by specific US work location, as well as additional factors, including job-related skills, experience, relevant education or training, and internal equity considerations.

Please note that the range listed above reflects only base salary. The total compensation package includes variable pay (where applicable), equity, plus a range of benefits, including medical, dental, vision, and financial. In addition, we offer perks such as generous stipends for health & fitness and learning & development, among others.

We're a new team building AI-assisted product experiences that help Datadog customers go from investigation to action, by enabling conversational workflows, guided remediations, and codefixes that address the root cause of production issues.

We're looking for a product-minded engineer to help us quickly define and ship applied AI experiences across chat, remediations, and codefixes. This role sits at the intersection of backend engineering, product development, and prompt engineering, with a strong emphasis on building reliable, production-quality AI systems.

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

What You'll Do:

• Work closely with product managers, designers, and engineers to build and iterate on AI-powered product experiences in Bits AI SRE.
• Develop customer-facing systems across chat, remediations, and codefixes that help users resolve production issues more quickly.
• Work on prompts, evaluation loops, and backend systems to make applied AI workflows reliable, useful, and production-ready.
• Prototype quickly, test what works in the real world, and iterate rapidly to ship new product capabilities.
• Build the infrastructure and product logic needed to connect AI outputs to meaningful actions, including operational remediations and generated code changes.
• Collaborate with partner teams across Datadog to expand remediation capabilities and integrate with systems that support investigation, automation, and code generation.
• Follow the latest developments in LLM prompting, agent design, and applied AI product development, and bring strong judgment about what is practical to use in production.

Who You Are:

• You're an engineer with at least 5 years of professional experience, with strong backend engineering skills and a product mindset. You have experience building production systems in Go (or similar) and have worked with LLM-based systems in practice.
• You are excited about applied AI and motivated by building product experiences that help users take meaningful action, not just generate insights.
• You have experience with prompt engineering, evaluation, and iteration for LLM-powered systems, and know how to improve quality through experimentation and feedback.
• You have strong engineering fundamentals and can build the systems needed to productionize AI features, including integrating model behavior into reliable user-facing products.
• You are comfortable operating in a fast-moving environment with high ambiguity, and enjoy prototyping, learning quickly, and shipping early versions of new ideas.
• You collaborate well with cross-functional partners and can work effectively with product, design, and engineering to shape both the user experience and the technical implementation.
• You care about product quality and user outcomes, and can balance speed with pragmatism when building new AI-powered workflows.
• Bonus: you have experience with Kubernetes or systems related to production remediation and operational automation.
• Requirement - Demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output.
• Plus - You’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products.

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

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

To conform to US export control regulations, candidates should be eligible for any required authorizations from the US government. This job is available in various departments within our company; to conform to US export control regulations, some of these roles may require candidates to be eligible for any required authorizations from the US government.

#LI-Hybrid

DigitalOcean is expanding its AI Infrastructure layer to support the next generation of AI-driven applications.

We are seeking a Senior Engineer 2 to play a key role in our AI Infrastructure Control Plane team. This team's mission is to orchestrate and operate compute fleet, storage and networking for running AI workloads - inference, training, fine-tuning - at scale. In this high-impact role, you will be a primary technical architect and leader, responsible for building the foundational control plane services that power our large Gen AI model hosting infrastructure.

This is a hands-on role, requiring you to be able to develop high quality software while availing of all the productivity boosts granted by the latest AI coding agents. You will bridge the gap between high-scale distributed systems and specialized AI inference orchestration to ensure our global platform remains simple, reliable, and accessible for developers building the next generation of AI applications.

What You’ll Do:

• Architectural Leadership: Drive the design and development of distributed services within our AI Infrastructure ecosystem, including complex orchestration for LLM inference and hosting services. Your focus will be the control plane orchestration of compute, networking, and storage for AI workloads.
• System Design: Create, refine, and assess system design proposals for our high-scale, multi-tenant inference cloud ecosystem, ensuring they meet rigorous standards for availability and resiliency.
• Operational Excellence: Lead the operational strategy for critical services, defining SLOs and leveraging advanced observability to maintain platform health in a high-scale environment.
• Cross-Functional Collaboration: Partner deeply with Product Management, TPMs, and Engineering Management peers to align technical roadmaps with business priorities.
• Strategic Growth: Work on new architecture initiatives that enable fleet optimization and help evolve DigitalOcean into a market leader for AI-native networking and infrastructure.

What You’ll Bring to DigitalOcean:

• Distributed Systems Mastery: Deep experience with distributed and cloud services, including messaging systems, databases, and infrastructure as code, observability, and security.
• Cloud Infrastructure Expertise: Advanced knowledge of cloud networking (VPCs, Load Balancers), containerization (Kubernetes), and cloud storage (block, object, NFS). Deep networking software experience (VLAN, RDMA, Ethernet, L3-L7 network protocols, etc) would be a plus.
• AI/ML Domain Knowledge: Proven experience building AI/ML products, specifically focusing on Gen AI platforms, LLM hosting, and inference workflows.
• Operational Track Record: Significant experience running customer-facing, high-availability services across multiple regions.
• Open Source Engagement: Experience integrating and building with open-source software and a bias for technical ownership.
• Software Engineering: Expert proficiency in GoLang or Python and familiarity with gRPC for service-to-service communication.

Compensation Range: 

• $139,200.00 - $174,000.00

*This is a remote role

JR: 2026-7623

#LI-Remote

DigitalOcean is expanding its AI Infrastructure layer to support the next generation of AI-driven applications. We are seeking a Senior Engineer 2 to join our AI Inference Engine Systems team. In this role, you will be a key technical leader responsible for designing, developing, and delivering high-scale, resilient data plane services that power our "Inference as a Service" offering. You will work at the intersection of distributed systems and specialized AI hardware to ensure our customers can deploy and scale their models with industry-leading performance and reliability. This is a hands-on role, requiring you to be able to develop high quality software while availing of all the productivity boosts granted by the latest AI coding agents. 

What You’ll Do:

• Technical Leadership: Act as a technical leader on the team, driving the end-to-end design, development, and delivery of critical data plane components hosting large generative AI models.
• System Design: Architect and refine system design proposals for our high-scale, multi-tenant AI inference cloud ecosystem, ensuring they meet rigorous availability and resiliency standards.
• Performance Optimization: Implement and optimize distributed inference hosting using techniques like tensor/data parallelism, KV cache optimizations, and smart routing.
• Collaboration: Work cross-functionally with Product Managers, customer-facing teams, and other engineering teams to align technical roadmaps with customer needs.
• Mentorship: Coach and mentor junior engineers, fostering a culture of technical excellence and continuous improvement.
• Operational Excellence: Maintain and operate critical, high-scale services, utilizing observability tools and defining SLOs to ensure superior platform health.

What You’ll Bring to DigitalOcean:

• Distributed Systems Expertise: Strong experience with microservices, messaging systems, databases, and infrastructure as code.
• AI/ML Domain Knowledge: Hands-on experience hosting large language or multimodal models using inference engines like vLLM, SGLang, or Modular.
• Inference Frameworks: Familiarity with distributed inference serving frameworks such as llm-d, NVIDIA Dynamo, or Ray Serve.
• Hardware & Interconnects: Understanding of GPU-level optimization and experience with interconnect technologies like NVlink, XGMI, or RoCE.
• Architecture Proficiency: Knowledge of common LLM architectures and optimization techniques (e.g., continuous batching, quantization).
• Software Engineering: Expert-level proficiency in GoLang or Python and familiarity with gRPC.
• Cloud Operations: Proven experience shipping customer-facing software products and running critical services in a high-scale environment similar to DigitalOcean.
• Open Source Mindset: Experience integrating and building with open-source software.

Compensation Range: 

• $167,200.00 - $209,000

*This is a remote role

JR: 2026-7591

#LI-Remote

DigitalOcean is seeking a Senior Engineer 2 to play a key technical role in our GPU Kernel and Performance team. DigitalOcean aims to be the Inference Cloud of choice for digitally native companies and you will help ensure we can offer the industry-leading performance for our inference services. You will be responsible for the architectural decisions that maximize throughput and minimize latency for the world’s most advanced large models. As an IC leader, you will act as a force multiplier for the engineering organization, solving the most complex bottlenecks in memory bandwidth and compute utilization while guiding the technical roadmap for our high-performance inference fleet.

What You’ll Do:

• Kernel Engineering: Design and implement high-performance GPU kernels using Triton and CUDA C++.
• Precision Optimization: Develop and deploy state-of-the-art quantization techniques (FP8, INT8, and experimental FP4) to double throughput without losing accuracy.
• Memory Magic: Optimize memory access patterns (SRAM vs. HBM3e) to eliminate bottlenecks in long-context attention mechanisms.
• Research to Production: Implement the latest architectural breakthroughs like FlashAttention-4 and TileLang directly into our production stack.

What You’ll Bring:

• Low-Level Mastery: Deep understanding of GPU architectures (SMs, Warp scheduling, Tensor Cores).
• The Toolkit: Expert-level Triton or CUDA. If you’ve contributed to the Triton compiler or wrote custom CUDA kernels for a major LLM, we want you.
• Math Foundation: Strong grasp of linear algebra and how it maps to parallel hardware.
• Performance Obsession: A track record of optimizing kernels to achieve >80% of theoretical hardware peak performance.

Compensation Range: 

• $167,200.00 to $209,000

*This is a remote role

JR:  2026-7593

#LI-Remote

The ML Observability team builds cutting-edge tools to monitor, explain, and improve AI systems in production, particularly those leveraging Large Language Models (LLMs) and generative AI. We provide robust, scalable observability for AI workloads, including drift detection and model evaluation, and behavior tracing, enabling customers to ship AI with confidence.

As a Staff Engineer, you’ll lead the development of new features and foundational capabilities within Datadog’s LLM Observability product. You will shape product direction, drive experimentation, and apply your deep understanding of both AI systems and software engineering to solve open-ended problems in the fast-moving AI landscape. Your work will directly impact how our customers monitor, troubleshoot, and optimize LLM-based applications in production.

Join us in building the foundational tools that make AI systems observable, understandable, and reliable in the real world.

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

What You’ll Do:

• Drive design and implementation of LLM observability features.

• Ideate, prototype, and scale new product features to provide insights and drive improvements for generative AI systems
• Work cross-functionally with other eng teams, product, UX, and applied science to iterate fast and find product-market fit
• Develop and extend tools for tracing, evaluating, and debugging LLMs
• Influence architecture decisions and mentor engineers to build resilient, high-performance systems
• Stay close to customer pain points and use those insights to guide product and engineering priorities
• Stay current with industry trends and advancements in machine learning and observability, driving innovation within the team

Who You Are:

• You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent experience
• Deep understanding of distributed systems and scalable backend architectures
• Hands-on experience building and shipping LLM-powered or GenAI applications. 
• Understanding of model internals, inference pipelines, evaluation techniques, and prompt engineering
• Ability to thrive in ambiguous, fast-changing spaces and have a product-oriented mindset
• You’re excited to shape the next generation of AI observability tools from the ground up
• Communicate clearly, think rigorously, and take pride in clean, maintainable code
• Experience with observability tools/platforms

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

Benefits and Growth:

• Get to build tools for software engineers, just like yourself. And use the tools we build to accelerate our development.
• Have a lot of influence on product direction and impact on the business .
• Work with skilled, knowledgeable, and kind teammates who are happy to teach and learn
• Competitive global benefits
• Continuous professional development

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

#LI-Hybrid

Datadog’s CodeGen team builds systems that use AI to read, understand, generate, and safely change real code — powering automated fixes, CI/PR automation, agentic remediation, and Infrastructure-as-Code workflows. The team ties code understanding to observability so assistants can explain problems and produce safe, auditable code changes.

We’re a new team building AI-assisted tools to make Datadog developers more effective, by autonomously generating tests, fixing bugs, and improving performance.

We’re looking for a product-minded generalist to help us quickly define and ship products that make all Datadog customers 10x developers.

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

What You’ll Do: 

• Lead a team of engineers responsible for building production systems that enable code understanding and automated code changes — from parsers and telemetry ingestion to model serving, evaluation, and PR automation.
• Drive technical direction and execution: set a clear roadmap, prioritize work, remove blockers, and raise quality and reliability bar for codegen features (safety, correctness, performance).
• Partner with product, applied research, infra/SRE, security, and other engineering teams to ship end-to-end experiences that safely apply model outputs (PRs, CI checks, automated apply flows).
• Build and maintain robust evaluation and monitoring pipelines (offline and online) to measure model quality, drift, and downstream correctness of code changes.
• Own hiring, performance development, 1:1s, and career growth for your reports; grow a high-performing, inclusive team.
• Be accountable for production readiness: on-call expectations, postmortems, SLIs/SLOs, and operational playbooks for codegen services.
• Maintain engineering rigor around data pipelines, model inputs, determinism, reproducibility, and reproducible CI/CD for model + infra changes.
  
  

Who You Are: 

• Proven experience in software engineering and applied science, with a focus on engineering LLM-based systems in production
• Demonstrated experience managing small teams of software engineers and/or applied scientists, with a track record of delivering high-quality products
• Strong software development skills and proficiency in Python and Go
• Strong understanding of machine learning theory, statistics, and fundamentals
• Excellent communication abilities to convey complex technical concepts clearly
• A collaborative mindset and proven experience in working in cross-functional teams
• A proactive approach with a passion for continuous learning and innovation
• Demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output
• You’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products

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

Benefits & Growth:

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits
  
  

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

#LI-Hybrid

We’re a new team building AI-assisted tools to make Datadog developers more effective, by autonomously generating tests, fixing bugs, and improving performance.

We’re looking for a product-minded generalist to help us quickly define and ship products that make all Datadog customers 10x developers.

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

What You’ll Do:

• Work closely with our users, designers, PMs, and data scientists on cutting edge and delightful LLM-based agents.
• Work on prompts, evals, and agent tools for working with code, and on the engineering to productionize these systems and integrate them with Datadog products.
• Prototype and see what works well in the real world, iterate closely with our users, and then ship.
• Follow the latest research on LLM prompting, agents, and code editing.
• Work with other Datadog teams to expand capabilities, for example integrating with the Datadog IDE extensions to deliver a CoPilot-like assistant, or Dynamic Instrumentation to collect more runtime data from production and pre-production environments.

Who You Are:

• You’re a backend engineer with at least 5 years of professional experience (Go, Java, Python) who values code simplicity and performance. You’ve shipped software that uses LLMs to production.
• You are passionate about Generative AI and its ability to help software engineers be more productive.
• You have relevant experience with Large Language Models, large-scale systems and data sets, machine learning, or adjacent fields.
• You are a great community ambassador and can drive hard technical conversations towards a pragmatic solution.
• You want to work in a fast, high-growth startup environment that respects its engineers and customers.
• You have experience writing developer tools (such as IDEs, static code analysis tools, or compilers).
• You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output
• You’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products

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

Benefits and Growth:

• Get to build tools for software engineers, just like yourself. And use the tools we build to accelerate our development.
• Have a lot of influence on product direction and impact on the business.
• Work with skilled, knowledgeable, and kind teammates who are happy to teach and learn.
• Competitive global benefits.
• Continuous professional development.
  
  

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

#LI-Hybrid

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

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

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

What You’ll Do:

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

Who You Are: 

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

Bonus Points:

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

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

Benefits and Growth: 

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

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

The opportunity

Datadog’s APM Experiences team owns the core product experience for Application Performance Monitoring — including distributed tracing, service representation, and more. We’re building a new wave of AI-powered capabilities that help customers detect, resolve, and prevent performance issues faster. In this role, you will lead end‑to‑end development of LLM- and Agent‑based features that can:

• Debug and investigate application performance issues down to the root cause, as both a developer assistant and a fully autonomous agent
• Proactively recommend performance and reliability-based optimizations to prevent the next incident
• Automatically create intelligent monitors and SLOs for the most important business flows and critical paths

This is a highly product‑minded engineering role: you’ll work from problem discovery and UX all the way to reliable, scalable production systems.

What you’ll do

• Shape AI experiences for APM. Design and ship LLM/agentic workflows that analyze traces, metrics, logs, and other telemetry to generate diagnoses, explanations, and guided fixes.
• Own the full loop. Prototype quickly, define success metrics and evals, run experiments, iterate, and ultimately productionize for scale and reliability.
• Build robust agent systems. Develop tools, retrieval and planning strategies, and guardrails; manage prompts/evals; design fallbacks and human‑in‑the‑loop paths.
• Integrate with Datadog’s platform. Leverage surfaces like Trace Explorer, Service Catalog, monitors, and workflows to deliver end‑to‑end value in the APM UI.
• Partner deeply. Collaborate with PM, Design, and partner teams to build cohesive experiences.
• Raise the bar on engineering. Write performant, maintainable backend code, own services in production, and improve reliability for high‑throughput, low‑latency data systems.

Who you are

Product‑minded engineer who ships AI to production

• 4+ years building backend or real-time ML systems; you value simplicity, correctness, and performance
• Proven experience delivering LLM/agent features to production (prompting, tooling, evals, safety/guardrails)
• Comfortable owning user journeys, iterating from prototype → alpha → GA, and measuring impact with clear product metrics
• You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output
• You’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products

Strong ML / applied science fundamentals

• Solid grasp of the ML lifecycle (task definition, dataset collection, modeling, evaluation, deployment, iteration) and statistics (experiment design, confidence intervals)
• Experience choosing/modeling the right technique for the job (e.g., anomaly detection, ranking/recommendation, NLP), and knowing when a heuristic beats a model
• Fluency with offline/online evals for AI systems; can build reliable golden sets and automatic regressions

Distributed systems & observability savvy

• Experience with microservices performance: tracing, latency breakdowns, concurrency, and resiliency patterns
• Proficient in Go, Java, or Python; strong API/service design; production ops (monitoring, alerting, on‑call rotation)

Nice to have

• Hands‑on with distributed tracing stacks (OpenTelemetry/Datadog APM), profilers, and logs/metrics pipelines
• Exposure to planning/agent frameworks, tool‑use orchestration, RAG, and retrieval/indexing for observability data
• Familiarity with SLO/SLA practices and incident response

Benefits and Growth:

• Get to build tools for software engineers, just like yourself. And use the tools we build to accelerate our development.
• Have a lot of influence on product direction and impact on the business.
• Work with skilled, knowledgeable, and kind teammates who are happy to teach and learn.
• Competitive global benefits.
• Continuous professional development.
  
  

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

#LI-Hybrid

At Klaviyo, we believe the future of software lies not in productivity tools for human users but in software that can run and optimize itself based on outcome or reward metrics. We’ve built the infrastructure and application that serve as the interface between businesses and consumers. We now have over 167,000 customers, billions of consumer profiles, and hundreds of billions of customer messages and follow-on conversion data. We are building state-of-the-art AI and machine learning technologies at Klaviyo to power marketing agent, service agent, and personalization products such as smart send time, audience optimization and product recommendations.

As a Machine Learning Platform Engineering Manager, you will lead a team of engineers building the core platforms, tools, and infrastructure that ML and AI engineers rely on to create advanced agents and models powering Klaviyo’s products. Your team will own platforms for training and deploying models and scaling systems that must operate reliably at Klaviyo’s data and traffic scale.

This is a backend-heavy, hands-on technical leadership role. You will manage and grow engineers, shape the technical roadmap for ML Platform, and collaborate closely with product, ML, data science, and infrastructure partners to deliver durable, high-leverage platforms that accelerate AI development across Klaviyo.

This role is hybrid and based in Boston.

How You’ll Make an Impact

• Enable AI / ML teams across Klaviyo to ship new models and ML-powered features faster, more safely, and more reliably.
• Evolve our ML and LLM platform architecture so teams can build, evaluate, and operate production ML systems with consistent patterns and best practices.
• Improve reliability, observability, and cost efficiency of ML training, inference, and other ML workloads that serve features like smart send time, audience optimization and product recommendations.
• Raise the bar on engineering and operational excellence for systems that support 167K+ customers and billions of events.
• Build and grow a high-performing, inclusive engineering team that operates with ownership, curiosity, and strong customer focus.

What You’ll Do

• Lead, coach, and grow a team of software / ML platform engineers (typically 6–10+ direct reports).
• Set clear goals, provide regular feedback, and support career development aligned with Klaviyo’s Engineering Career Architecture.
• Stay hands-on with the technology: participate in design reviews, code reviews, and occasionally ship code for critical paths or prototypes.
• Partner with product and technical leaders to define and evolve the ML Platform strategy and roadmap, balancing short-term delivery with long-term platform investments.

Learn more about the team’s work in our blogs on Ray Jobs & Ray Tune.

Who You Are

We encourage candidates to apply even if they do not meet all the qualifications listed. ML Platform and MLOps are rapidly evolving spaces and we are all constantly learning.

• 7+ years of experience as a software engineer, ML platform engineer, or similar, building backend or distributed systems.
• 2+ years of experience in a formal people leadership role
• Demonstrated experience launching and operating AI/ML systems in production at scale
• Strong proficiency in Python (or a similar backend language such as Go, Java, or Scala).
• Experience with cloud infrastructure and platform fundamentals (AWS, kubernetes):
• Familiarity with data-intensive systems: streaming or batch processing, data modeling, and designing systems with large-scale data in mind (spark, ray-data).
• Comfortable collaborating with cross-functional technical partners (ML, product, infra, security).
• Strong communication skills and a track record of driving clarity and alignment across teams.

At Compass International Holdings (CIH), we connect buyers and sellers with the right agents at the right time. The Lead Nurturing & Engagement team is responsible for keeping leads warm and actionable before they are assigned to an agent — ensuring no qualified lead goes cold due to slow response, lack of follow-up, or poor timing. We own the full pre-CRM nurturing funnel: from the moment a lead enters the system to the moment it is ready to be handed off to an agent.

As a Senior Software Engineer on this team, you will design and build the systems that power automated re-engagement flows, unclaimed lead management, human-assisted concierge experiences via AWS Connect, and lead readiness scoring. Your work directly impacts how many leads convert to closed transactions — making this one of the highest-leverage engineering roles on the Leads platform.

At Compass, You Will: 

• Design and build the pre-CRM nurturing pipeline — automated flows that move leads from cold → warm → hot before agent assignment
• Own and operate the AWS Connect integration — the human-assisted concierge experience that handles complex or high-value leads requiring live engagement
• Build and maintain unclaimed lead management — automated escalation, retry, and re-engagement for leads that have not yet been matched to an agent
• Develop lead readiness scoring — the signals and models that determine when a lead is "hot enough" for agent assignment
• Architect automated nurture sequences across SMS, email, and voice channels, coordinating with our communications platform
• Collaborate with data and ML teams to incorporate behavioral signals and conversion predictions into nurturing decisions
• Operate in a scalable engineering culture that leverages modern principles of decoupled systems and automated CI/CD/testing/monitoring

What We Look For: 

• BS in Computer Science, Software Engineering, or equivalent practical experience
• 7+ years of experience developing comprehensive, well-tested, and high-performance software applications with languages Python or NodeJS 
• Experience with gRPC, Thrift, and other server-to-server communication protocols
• Experience with Kafka and event-driven systems — particularly for building stateful automation pipelines and re-engagement flows
• 3+ years using AWS / cloud technologies
• High proficiency designing and implementing microservices that are well tested, fault tolerant, and scalable
• Understanding and adherence to industry-standard best practices in software development and architecture
• Believes in good software development requiring thorough testing, robust resiliency features, effective monitoring capabilities, comprehensive documentation, and close collaboration
• Experience with Scrum/Agile development methodologies
• Strong critical thinking skills, great communication skills, and passion for operational excellence

Preferred Qualifications

• AWS Connect or similar experience: Prior experience building on or integrating with AWS Connect, Twilio Flex, Genesys, or similar Contact Center as a Service platforms — including call flows, agent routing, and real-time event handling
• Lead lifecycle and CRM systems: Familiarity with CRM platforms (Salesforce, HubSpot, or similar) and how leads flow through lifecycle stages from first contact to close; ability to design clean ownership boundaries between nurturing and CRM systems
• Engagement automation pipelines: Experience building automated outreach pipelines — nurture sequences, drip campaigns, or re-engagement flows — triggered by behavioral signals across SMS, email, and voice channels
• Lead scoring or ML-based ranking: Experience working with or building scoring models that rank or classify leads by intent, readiness, or conversion probability
• Communication platform integration: Hands-on experience with Twilio, Sinch, Amazon Pinpoint, or similar platforms for programmatic SMS, MMS, email, and voice delivery at scale
• Real estate or proptech domain knowledge: Familiarity with real estate transaction flows, agent-consumer interactions, or lead-to-close lifecycle
• AI agent development: Familiarity with agentic AI frameworks (LangChain, LlamaIndex, AutoGen, or similar) for building autonomous or semi-autonomous workflows — such as AI-assisted concierge routing or automated follow-up decision-making
• AI and LLM integration: Experience integrating large language models (LLMs) or AI APIs (OpenAI, Anthropic, AWS Bedrock) into production systems — particularly for personalizing outreach content, generating dynamic nurture sequences, or powering conversational engagement flows
• Experience with Python or NodeJS development

Compensation: The base pay range for this position is $176,000-$196,100; however, base pay offered may vary depending on job-related knowledge, skills, and experience. Bonuses and restricted stock units may be provided as part of the compensation package, in addition to a full range of benefits. Base pay is based on market location. Minimum wage for the position will always be met.

Why This Role, Why Now

GTM Data Strategy & Operations stood up from scratch with no predecessor. Today the function runs on three offshore contractors and zero FTEs, managed by a single leader who is simultaneously building the agentic infrastructure, operating it in production, and driving major initiatives (hierarchy redesign, data quality assessment, vendor optimization).

The operating model is deliberately agentic AI–first: a multi-agent pipeline (Cartographer, Sentinel, Resolver, Reporting) handles detection, enrichment, hierarchy mapping, and conflict resolution at scale. This is not a future-state vision, these agents are live and processing enterprise account families in production today.

The problem: one person cannot build, operate, and extend this system while also managing strategic workstreams. The function currently covers only core Tier‑1 fields. Dozens of account, contact, and lead signals remain unaddressed. Every pipeline run, every failure diagnosis, and every offshore handoff flows through a single point of failure.

This role is the first onshore execution hire for an agent operator who can keep the system running, improve it, and extend detection and resolution coverage as GTM leadership prioritizes new data elements.

Role Summary

Sit between AI systems and GTM data. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don’t build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.

Core Responsibilities

Agent Pipeline Operations

• Run and monitor production pipeline sessions (Cartographer, Sentinel, Resolver) across scheduled cadences; diagnose and resolve failures (API errors, session timeouts, data anomalies) without escalating to the function lead.
• Execute pipeline runs in Claude Claude and tmux; manage long-running batch processes; interpret logs and output to confirm data integrity before downstream handoff.
• Maintain pipeline orchestration scripts and configuration; extend agent coverage as new data elements are prioritized by GTM leadership.

Agent Tuning & Improvement

• Refine detection rules, prompt logic, and confidence thresholds based on output analysis and false-positive/negative patterns.
• Evaluate agent accuracy by segment (Enterprise vs. MM/SMB) and recommend rule or workflow changes backed by evidence.
• Run bake-offs (vendor vs. AI enrichment) to optimize cost, coverage, and accuracy; document results for decision-making.

Sentinel → Offshore Resolution Loop

• Own the handoff between Sentinel detection output and Concentrix triage queues; define queue structure, priority tiers, and resolution instructions.
• Monitor offshore resolution quality and throughput; refine detection rules based on patterns surfaced through triage.
• Close the feedback loop: track resolution outcomes back to agent configuration to reduce recurring false positives and improve detection precision.

Data Quality & Enrichment Operations

• Maintain ops-only staging fields; manage the promote-to-production flow with audit controls.
• Design and run AI-assisted enrichment workflows (Clay + LLM prompts) with evidence links and confidence thresholds.
• Monitor fill-rate, sampled accuracy, freshness, and cost-per-record by source and segment; surface vendor performance issues and recommend changes.
• Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.

Cross-Functional Partnership

• GTM Systems (SFDC): field configuration, permission sets, automation, flows.
• Data Engineering: source availability, ID mapping, lineage (no pipeline coding).
• Reporting: define metrics and acceptance criteria; partner on dashboard requirements.

What to Expect

This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You’ll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.

Success Metrics (6–12 Months)

Pipeline Reliability

• Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
• Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).

Detection & Resolution Quality

• Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
• Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
• False-positive rate decreases through feedback-loop refinement.

Data Quality Outcomes

• Tier-1 field fill-rates: Country ≥95%; Vertical ≥90% at ≥85% sampled accuracy; Revenue bands ≥90%.
• Hierarchy coverage 65–80%+ across target segments.
• Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.

Qualifications

Required

• 3–6 years in Data Ops, Sales Ops, or GTM Ops with hands-on data quality ownership for account and contact data.
• Proficiency with Snowflake (SQL for querying, analysis, validation) and SFDC (object model, field configuration, data flows).
• Working experience with Claude Code or comparable LLM-based tooling in an operational (not just experimental) context.
• Experience designing and running AI-assisted enrichment workflows (e.g., Clay + LLM prompts) and evaluating accuracy/coverage.
• Comfort operating in a command-line environment: tmux, shell scripts, log analysis, batch process monitoring.
• Process design mindset with a bias toward measurable outcomes; strong written communication.

Strong Plus

• Experience with account/contact data vendors (D&B, ZoomInfo, Clearbit, StoreLeads) and waterfall enrichment logic.
• Python for QA scripting, sampling, or light automation.
• Familiarity with prompt engineering, confidence scoring, and AI guardrails (evidence capture, versioned prompts, QA sampling gates).

Tool Stack

• Core: Snowflake (SQL), SFDC, Claude Code, Clay
• Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
• Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
• Nice to Have: Python, SOQL, prompt engineering frameworks
• AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.

Why This Role, Why Now

GTM Data Strategy & Operations stood up from scratch with no predecessor. Today the function runs on three offshore contractors and zero FTEs, managed by a single leader who is simultaneously building the agentic infrastructure, operating it in production, and driving major initiatives (hierarchy redesign, data quality assessment, vendor optimization).

The operating model is deliberately agentic AI–first: a multi-agent pipeline (Cartographer, Sentinel, Resolver, Reporting) handles detection, enrichment, hierarchy mapping, and conflict resolution at scale. This is not a future-state vision, these agents are live and processing enterprise account families in production today.

The problem: one person cannot build, operate, and extend this system while also managing strategic workstreams. The function currently covers only core Tier‑1 fields. Dozens of account, contact, and lead signals remain unaddressed. Every pipeline run, every failure diagnosis, and every offshore handoff flows through a single point of failure.

This role is the first onshore execution hire for an agent operator who can keep the system running, improve it, and extend detection and resolution coverage as GTM leadership prioritizes new data elements.

Role Summary

Sit between AI systems and GTM data. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don’t build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.

Core Responsibilities

Agent Pipeline Operations

• Run and monitor production pipeline sessions (Cartographer, Sentinel, Resolver) across scheduled cadences; diagnose and resolve failures (API errors, session timeouts, data anomalies) without escalating to the function lead.
• Execute pipeline runs in Claude Claude and tmux; manage long-running batch processes; interpret logs and output to confirm data integrity before downstream handoff.
• Maintain pipeline orchestration scripts and configuration; extend agent coverage as new data elements are prioritized by GTM leadership.

Agent Tuning & Improvement

• Refine detection rules, prompt logic, and confidence thresholds based on output analysis and false-positive/negative patterns.
• Evaluate agent accuracy by segment (Enterprise vs. MM/SMB) and recommend rule or workflow changes backed by evidence.
• Run bake-offs (vendor vs. AI enrichment) to optimize cost, coverage, and accuracy; document results for decision-making.

Sentinel → Offshore Resolution Loop

• Own the handoff between Sentinel detection output and Concentrix triage queues; define queue structure, priority tiers, and resolution instructions.
• Monitor offshore resolution quality and throughput; refine detection rules based on patterns surfaced through triage.
• Close the feedback loop: track resolution outcomes back to agent configuration to reduce recurring false positives and improve detection precision.

Data Quality & Enrichment Operations

• Maintain ops-only staging fields; manage the promote-to-production flow with audit controls.
• Design and run AI-assisted enrichment workflows (Clay + LLM prompts) with evidence links and confidence thresholds.
• Monitor fill-rate, sampled accuracy, freshness, and cost-per-record by source and segment; surface vendor performance issues and recommend changes.
• Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.

Cross-Functional Partnership

• GTM Systems (SFDC): field configuration, permission sets, automation, flows.
• Data Engineering: source availability, ID mapping, lineage (no pipeline coding).
• Reporting: define metrics and acceptance criteria; partner on dashboard requirements.

What to Expect

This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You’ll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.

Success Metrics (6–12 Months)

Pipeline Reliability

• Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
• Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).

Detection & Resolution Quality

• Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
• Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
• False-positive rate decreases through feedback-loop refinement.

Data Quality Outcomes

• Tier-1 field fill-rates: Country ≥95%; Vertical ≥90% at ≥85% sampled accuracy; Revenue bands ≥90%.
• Hierarchy coverage 65–80%+ across target segments.
• Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.

Qualifications

Required

• 3–6 years in Data Ops, Sales Ops, or GTM Ops with hands-on data quality ownership for account and contact data.
• Proficiency with Snowflake (SQL for querying, analysis, validation) and SFDC (object model, field configuration, data flows).
• Working experience with Claude Code or comparable LLM-based tooling in an operational (not just experimental) context.
• Experience designing and running AI-assisted enrichment workflows (e.g., Clay + LLM prompts) and evaluating accuracy/coverage.
• Comfort operating in a command-line environment: tmux, shell scripts, log analysis, batch process monitoring.
• Process design mindset with a bias toward measurable outcomes; strong written communication.

Strong Plus

• Experience with account/contact data vendors (D&B, ZoomInfo, Clearbit, StoreLeads) and waterfall enrichment logic.
• Python for QA scripting, sampling, or light automation.
• Familiarity with prompt engineering, confidence scoring, and AI guardrails (evidence capture, versioned prompts, QA sampling gates).

Tool Stack

• Core: Snowflake (SQL), SFDC, Claude Code, Clay
• Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
• Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
• Nice to Have: Python, SOQL, prompt engineering frameworks
• AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.

Why This Role, Why Now

GTM Data Strategy & Operations stood up from scratch with no predecessor. Today the function runs on three offshore contractors and zero FTEs, managed by a single leader who is simultaneously building the agentic infrastructure, operating it in production, and driving major initiatives (hierarchy redesign, data quality assessment, vendor optimization).

The operating model is deliberately agentic AI–first: a multi-agent pipeline (Cartographer, Sentinel, Resolver, Reporting) handles detection, enrichment, hierarchy mapping, and conflict resolution at scale. This is not a future-state vision, these agents are live and processing enterprise account families in production today.

The problem: one person cannot build, operate, and extend this system while also managing strategic workstreams. The function currently covers only core Tier‑1 fields. Dozens of account, contact, and lead signals remain unaddressed. Every pipeline run, every failure diagnosis, and every offshore handoff flows through a single point of failure.

This role is the first onshore execution hire for an agent operator who can keep the system running, improve it, and extend detection and resolution coverage as GTM leadership prioritizes new data elements.

Role Summary

Sit between AI systems and GTM data. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don’t build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.

Core Responsibilities

Agent Pipeline Operations

• Run and monitor production pipeline sessions (Cartographer, Sentinel, Resolver) across scheduled cadences; diagnose and resolve failures (API errors, session timeouts, data anomalies) without escalating to the function lead.
• Execute pipeline runs in Claude Claude and tmux; manage long-running batch processes; interpret logs and output to confirm data integrity before downstream handoff.
• Maintain pipeline orchestration scripts and configuration; extend agent coverage as new data elements are prioritized by GTM leadership.

Agent Tuning & Improvement

• Refine detection rules, prompt logic, and confidence thresholds based on output analysis and false-positive/negative patterns.
• Evaluate agent accuracy by segment (Enterprise vs. MM/SMB) and recommend rule or workflow changes backed by evidence.
• Run bake-offs (vendor vs. AI enrichment) to optimize cost, coverage, and accuracy; document results for decision-making.

Sentinel → Offshore Resolution Loop

• Own the handoff between Sentinel detection output and Concentrix triage queues; define queue structure, priority tiers, and resolution instructions.
• Monitor offshore resolution quality and throughput; refine detection rules based on patterns surfaced through triage.
• Close the feedback loop: track resolution outcomes back to agent configuration to reduce recurring false positives and improve detection precision.

Data Quality & Enrichment Operations

• Maintain ops-only staging fields; manage the promote-to-production flow with audit controls.
• Design and run AI-assisted enrichment workflows (Clay + LLM prompts) with evidence links and confidence thresholds.
• Monitor fill-rate, sampled accuracy, freshness, and cost-per-record by source and segment; surface vendor performance issues and recommend changes.
• Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.

Cross-Functional Partnership

• GTM Systems (SFDC): field configuration, permission sets, automation, flows.
• Data Engineering: source availability, ID mapping, lineage (no pipeline coding).
• Reporting: define metrics and acceptance criteria; partner on dashboard requirements.

What to Expect

This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You’ll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.

Success Metrics (6–12 Months)

Pipeline Reliability

• Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
• Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).

Detection & Resolution Quality

• Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
• Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
• False-positive rate decreases through feedback-loop refinement.

Data Quality Outcomes

• Tier-1 field fill-rates: Country ≥95%; Vertical ≥90% at ≥85% sampled accuracy; Revenue bands ≥90%.
• Hierarchy coverage 65–80%+ across target segments.
• Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.

Qualifications

Required

• 3–6 years in Data Ops, Sales Ops, or GTM Ops with hands-on data quality ownership for account and contact data.
• Proficiency with Snowflake (SQL for querying, analysis, validation) and SFDC (object model, field configuration, data flows).
• Working experience with Claude Code or comparable LLM-based tooling in an operational (not just experimental) context.
• Experience designing and running AI-assisted enrichment workflows (e.g., Clay + LLM prompts) and evaluating accuracy/coverage.
• Comfort operating in a command-line environment: tmux, shell scripts, log analysis, batch process monitoring.
• Process design mindset with a bias toward measurable outcomes; strong written communication.

Strong Plus

• Experience with account/contact data vendors (D&B, ZoomInfo, Clearbit, StoreLeads) and waterfall enrichment logic.
• Python for QA scripting, sampling, or light automation.
• Familiarity with prompt engineering, confidence scoring, and AI guardrails (evidence capture, versioned prompts, QA sampling gates).

Tool Stack

• Core: Snowflake (SQL), SFDC, Claude Code, Clay
• Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
• Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
• Nice to Have: Python, SOQL, prompt engineering frameworks
• AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.

This is a part-time consulting role for 20hr/week. Duration of the project will be approximately 3-4 months. Preference will go to candidates in the Boston area. 

Your impact:

As the AI Adoption Consultant, you will bridge the gap between cutting-edge artificial intelligence and the complex workflows of a modern biotechnology organization. Your mission is to accelerate the integration of AI tools across the enterprise, moving beyond the "hype" to deliver tangible improvements in operational efficiency and scientific output.

You will partner with leaders in Research, Clinical Operations, Finance, IT, and People & Culture to identify high-impact opportunities, design implementation roadmaps, and lead a culture-shifting training program to ensure every employee is "AI-fluent."

Your day-to-day:  

• Conduct deep-dive discovery sessions with functional leads (Finance, IT, Research, etc.) to map existing workflows and identify bottlenecks.
• Build a prioritized "AI Opportunity Roadmap" that balances quick wins with long-term strategic shifts.
• Evaluate and recommend third-party AI tools or internal LLM applications that meet the specific security and compliance standards of the biotech industry.
• Collaborate with the CIO and IT team to ensure AI deployments are scalable, secure, and integrated into the existing tech stack.
• Develop and deliver tailored training modules for diverse audiences—from "AI for Scientists" to "Prompt Engineering for Finance Professionals."
• Create "Office Hours" and internal communities of practice to foster continuous learning and peer-to-peer knowledge sharing.
• Work with Legal and IT to establish clear guidelines for the ethical use of AI, particularly regarding sensitive data and intellectual property.

Must-Haves:

• Experience: 5+ years of experience in digital transformation, management consulting, or a similar role, with at least 1-2 years focused specifically on AI/ML implementation.
• Industry Knowledge: Direct experience in the Biotech or Pharmaceutical sector is highly preferred.
• Technical Fluency: A strong understanding of Large Language Models (LLMs), Generative AI tools, and data orchestration. You don't need to be a coder, but you must be able to speak the language of both data scientists and end-users.
• Communication: Exceptional ability to translate complex technical concepts into "business value" for non-technical stakeholders.
• Education: Bachelor’s degree in a relevant field (CS, Data Science, Business, or Life Sciences).

Location: Boston Hybrid (3 days/week in office)

Why You Should Join the Team

The Klaviyo Conversational AI Platform team is building the foundation for how Klaviyo customers plan, create, edit, and iterate on their marketing strategy through an in‑app, AI‑powered conversational experience. We own the native chat surface, the agent orchestrator, and the agentic UI framework that other teams across Klaviyo will plug into.

 By joining the Klaviyo Conversational AI Platform team, you will have the opportunity to have a direct impact on over 170,000 brands that use Klaviyo every day to engage with their customers. You’ll also help define and build Klaviyo’s Autonomous Marketing platform: an ecosystem of LLM‑powered agents that can analyze, create, and optimize marketing programs across campaigns, flows, forms, and more.

As the Engineering Manager for Klaviyo Conversational AI Platform, you will lead a fullstack team that owns the main Conversational AI Platform UI while designing the core intent-routing orchestrator and shared agent platform that other teams use to plug in.

This role requires excellent communication and collaboration skills. You will work closely with product managers, product designers, and many other engineering teams to extend and drive evolution of the product.

How You Will Make a Difference

• Lead a team of engineers delivering fullstack, customer-facing features used across Klaviyo’s Conversational AI Platform product offering
• Set the technical direction for the orchestrator that handles intent detection, routing, planning, and multi‑step LLM workflows across many expert agents.
• Own end-to-end execution: scope initiatives, break down work, align dependencies, and deliver high-quality releases on predictable timelines
• Drive architectural decisions that prioritize scalability, reliability, and maintainability across services, data models, and shared component systems
• Partner closely with Product and Design to translate vision into intuitive, performant user experiences
• Drive cross-team collaboration with Engineering to ensure the smooth integration of AI agents across teams.
• Grow and mentor engineers, fostering a high-performance and inclusive team culture
• Collaborate across Klaviyo to align roadmaps and ship cohesive, customer-delighting solutions
• Contribute hands-on via design docs, code reviews, and technical spikes to de-risk complex bets and model engineering excellence

Who You Are

• 8+ years of software engineering experience with strong frontend and backend skills
• 5+ years of engineering management experience, ideally managing fullstack teams.
• Familiarity with frontend technologies like React, TypeScript, and state management tools such as Redux
• Comfortable contributing to backend systems using Python and Django, FastAPI (or equivalent)
• Skilled in system design, reusable component architecture, and scalable frontend platforms
• Familiarity DevOps tooling, cloud infrastructure (AWS), Terraform, etc.
• Proven track record delivering complex features in fast-paced, product-led environments
• Clear communicator, collaborative leader, and committed to continuous team improvement
• Experience designing or leading systems that use LLMs, tools, or agent‑like workflows

Technologies We Use

• React, TypeScript, Redux
• Python, Django, FastAPI, Langchain
• MySQL, Redis, RabbitMQ, Pulsar, 
• GraphQL, RESTful APIs
• AWS, Terraform, Kubernetes

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

• Lead and manage a team of full-stack and AI engineers and directly manage some workstreams yourself.
• Collaborate on project planning, define achievements, identify dependencies, and ensure business goals are met.
• Actively participate in interviewing, hiring, and team leveling.
• Develop SMART goals and career development plans for direct reports.
• Develop and execute technical strategies aligned with Klaviyo's business goals, ensuring exceptional products and a reliable, user-friendly AI systems roadmap.
• Engage with internal and external stakeholders to understand needs, resolve blockers, manage expectations, and translate customer needs into valuable AI-driven roadmaps and solutions.
• Advise the team on modeling, system design, experimental design, and implementation decisions.
• Improve engineering-wide processes such as recruiting, performance development, communication, and agile development.
• Stay current with emerging AI trends and technologies, identifying and leveraging opportunities within Klaviyo’s ecosystem.

• Experience building AI-driven product features in a full-stack capacity and leading teams who have built scaled systems.
• 5+ years of experience in software engineering, 2+ years of experience  managing engineering teams, including remote members and lead engineers.
• Exceptional communication skills, capable of collaborating with diverse stakeholders to translate business/product roadmaps into AI-driven technical roadmaps and solutions that add significant customer and business value.
• Motivated to grow the team through developing current members and attracting new talent to expand capabilities.
• Eager to engage with code, understand the product area, and provide applicable technical mentorship.
• In-depth understanding of common engineering architectures for highly scalable systems.
• Proven ability to lead and deliver complex, multi-quarter projects involving at least two teams.
• Comfortable conducting performance reviews and mentoring team members at all levels.

• We love recovering entrepreneurs. If you’ve ever run your own thing, you’ll have company on our team.

Boston, MA - Hybrid Role

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Channel Manager at Unframe 

Location: East Coast, United States, Remote 

About Unframe

Unframe is an AI-first startup helping the world’s largest enterprises bring LLM-powered applications to life in days - not months. We combine the speed of a product company with the flexibility of a consultancy, helping customers move from idea to deployed AI systems faster than anyone else in the market.

Backed by Bessemer, Craft, and TLV Partners with $50M in Series A funding, we’re building a fast-growing, revenue-generating company working with Fortune 500 customers globally

About the Role 

The Channel Manager will build, enable, and scale Unframe’s partner ecosystem. This role is responsible for developing strategic partnerships (VARs, GSIs, MSPs and technology partners), driving sourced and influenced revenue, and ensuring partners are effectively enabled to position and sell Unframe solutions.

You’ll work cross-functionally with Sales, Marketing, Product, and Customer Success to create a repeatable, scalable partner motion.

What you’ll do:

Partner Strategy & Recruitment

• Develop and execute Unframe’s channel strategy aligned to revenue goals
• Identify, recruit, and onboard high-value channel partners
• Build joint business plans with key partners
• Establish clear partner segmentation and coverage models

Enablement & Activation

• Create and deliver partner enablement programs (sales training, technical onboarding, certifications)
• Equip partners with positioning, messaging, and competitive differentiation
• Support partners in pipeline generation and deal execution
• Drive joint marketing campaigns and co-selling initiatives

Revenue & Performance Management

• Own partner-sourced and partner-influenced revenue targets
• Forecast channel pipeline and performance metrics
• Establish KPIs and reporting dashboards
• Ensure alignment between direct sales and channel teams

Cross-Functional Collaboration

• Work closely with Sales to ensure clean rules of engagement
• Partner with Marketing on demand generation programs
• Provide product feedback based on partner/customer insights
• Collaborate with Customer Success on joint account expansion strategies

What we are looking for:

• 5+ years in channel sales, partnerships, or ecosystem development (B2B SaaS or AI preferred)
• Proven track record of building and scaling partner revenue
• Experience working with VARs, GSIs, MSPs, or cloud providers
• Strong understanding of enterprise sales cycles
• Excellent relationship-building and negotiation skills 

Nice-to-Have Skills:

• Experience in AI, data, or enterprise technology
• Familiarity and success selling and recruiting into VAR ecosystems
• Experience in early-stage or high-growth startups

Ready to Build the Future?

If this opportunity excites you, apply now!

Why Join Unframe?

• Real ownership from day one. Drive decisions, shape ideas and make them work.
• Your fastest learning years. Build alongside founders and VPs in a space that moves weekly.
• You'll have the opportunity to write and build what comes next.

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

Company Overview

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

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

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

Principal Quality Automation Architect

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

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

This position is not eligible for immigration sponsorship.

What You’ll Do:

Automation Architecture

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

AI Evaluation Infrastructure

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

Test Data & Environment Strategy

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

Engineering Partnership

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

You Have:

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

You Are:

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

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

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

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

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

#LI-HA1

Role overview

CarGurus is transforming how the company operates by embedding AI into workflows across every function — product, engineering, revenue, finance, support. It’s a software engineering role where the domain is the entire business. You’ll work directly with teams across the org to understand how work actually gets done, identify where AI can change the unit of work or shift where judgment lives, and build the systems that make it real. Some of that is agent-based automation. Some of it is workflow redesign. All of it requires someone who can architect and ship production-quality software, not stitch together SaaS integrations. This is not a Salesforce developer, Boomi connector, or low-code/no-code position, nor is it an enterprise architect or ML model training role.

This is a small, high-autonomy team reporting to the Head of AI Solutions. You’ll have outsized influence on how a publicly traded company with ~$1B in revenue adopts AI — not as a pilot or a demo, but as a structural change in how the business runs. You will be successful here if you are a product-caliber engineer who finds internal workflow transformation genuinely interesting — not a consolation prize, but the most leveraged place to be right now. You’d rather build something real that 200 people use tomorrow than something polished that ships in 6 months.

What you'll do

• Design and build AI-powered workflows and agent-based systems that transform how teams operate across the company.
• Partner directly with stakeholders in revenue, product, engineering, finance, and operations to gather requirements, map existing workflows, and identify high-leverage opportunities for AI.
• Build end-to-end — from scoping and architecture through implementation and deployment. This team ships, it doesn’t hand off specs.
• Work with AI APIs and platforms (Anthropic/Claude, AWS Bedrock, and others) to build agentic systems, automation pipelines, and intelligent tooling.
• Operate across a messy, heterogeneous environment — AWS infrastructure, Salesforce, internal services — without waiting for everything to be clean before you start building.
• Help shape the technical strategy and standards for AI Solutions as a function.

What you'll bring

• 5+ years of relevant software engineering experience.
• Strong software engineering fundamentals — you’ve built and shipped production systems, not just prototypes.
• Proficiency in Python as the primary language for this work; comfort with additional languages is a plus.
• Experience designing APIs, services, and data pipelines. You think in systems, not scripts.
• AWS fluency — you’re comfortable working within AWS infrastructure and services.
• Hands-on experience building with LLM APIs, agent frameworks, or AI-powered applications — not just experimentation, but something that ran in production or was used by real people.
• Ability to operate with high autonomy. You’ll scope your own work, make architectural decisions, and own outcomes.
• Willingness to work across the full stack of a business — including systems like Salesforce that aren’t glamorous but are where the high-impact problems live.

Nice-to-haves:

• Experience with workflow orchestration or automation at scale.
• Familiarity with Salesforce APIs and integration patterns (not Salesforce administration).
• Background in recommendations, search, data science, or ML engineering.
• Experience working cross-functionally with non-technical stakeholders.

VP Marketing 

Location: US (East Coast Preferred )

About Unframe:

Unframe is an AI-first startup helping the world’s largest enterprises bring LLM-powered applications to life in days - not months. We combine the speed of a product company with the flexibility of a consultancy, helping customers move from idea to deployed AI systems faster than anyone else in the market.

Backed by Bessemer, Craft, and TLV Partners with $50M in Series A funding, we’re building a fast-growing, revenue-generating company working with Fortune 500 customers globally

About the role

Lead Unframe’s global marketing strategy. 

What you’ll do

• Own and execute Unframe’s global marketing strategy, aligned to company growth and revenue goals.
  
  
• Lead and scale the global marketing function, including team development, planning, investment, and organisational structure.
  
  
• Partner closely with Sales, Product, and executive leadership to strengthen category positioning, align priorities, and drive pipeline.
  
  
• Oversee demand generation, product marketing, PR, marketing operations, website and brand/communications strategy
  
  

What we are looking for

• VP-level marketing leadership experience with ownership of strategy, budget, headcount, and measurable revenue impact.
  
  
• Strong track record leading marketing across both US and international markets
  
  
• Deep enterprise B2B marketing experience within startup and high-growth environments.
  
  
• Expertise in enterprise technology, AI, technical buyer journeys, and product/category positioning.
  
  
• Strong cross-functional leadership and ability to operate effectively in fast-moving, ambiguous, high-ownership environments.

Why join us

Real ownership from day one. Drive decisions, shape ideas and make them work.

• Your fastest learning years. Build alongside founders in a space that moves weekly.
• You'll have the opportunity to write and build what comes next.

Ready to Build the Future?

If this opportunity excites you, apply now!

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain" of this engine. You will work with state-of-the-art foundation models to extract insights from Motional’s driving data, working at the intersection of large-scale representation learning and data retrieval. By building smarter mining tools and efficient data pipelines, you will accelerate the model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Build and Train ML Pipelines: Develop, train, and fine-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR). Focus on implementing supervised and self-supervised learning approaches to improve data search and retrieval.
• Support Model Deployment: Implement scalable data preprocessing and augmentation pipelines. Assist in applying standard optimization techniques (e.g., batch inference, quantization) to ensure models run efficiently in production environments.
• Data Mining & Analysis: Help develop embedding-based search tools and "active learning" workflows to identify critical driving scenarios.
• Monitor Production Performance: Help build and maintain dashboards to monitor model health, data drift, and system performance. Identify regressions and assist in the operational support of our data mining services.
• Learn and Apply Best Practices: Follow software engineering standards (version control, CI/CD, unit testing) for ML code. Participate in code reviews and contribute to technical documentation.
• Collaborate Across Teams: Work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable, scalable engineering solutions.
  
  

What We're Looking For (Must-Haves):

• BS or MS in Computer Science, Machine Learning, or a related field.
• Hands-on experience with PyTorch (preferred) or TensorFlow/JAX. You should be comfortable training models and evaluating them using standard metrics.
• Strong proficiency in Python with the ability to write clean, modular, and well-documented code.
• Working knowledge of version control, unit testing, and basic software design patterns.
• Experience working with large datasets, including proficiency in SQL and data libraries like Pandas and NumPy.
• A solid grasp of the full ML lifecycle, from data cleaning and feature engineering to validation and deployment basics.
• A proactive learner who thrives on constructive feedback and is eager to grow within a high-stakes engineering environment.
  
  

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publication in top-tier conferences (e.g., ICCV, CVPR, ECCV)
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

We are looking for a Senior Software Engineer to develop and deliver automated workflows, intelligent tooling and insightful analytics that enables our autonomy stack to scale. You will play a pivotal role in transforming massive amounts of data into actionable engineering insights, ensuring our software releases are robust, validated, and ready for the road.

What you'll be doing:

• Develop Intelligent Tooling: You will design and implement sophisticated automation to categorize and explain complex system behaviors by leveraging advanced data modeling to accelerate solutions.
• Drive Analytics: You’ll lead the creation of high-impact performance visualization tools that translate raw metrics into clear, actionable insights regarding on-road performance.
• Streamline Release Engineering: Your expertise will help the team evolve our release processes, moving toward a highly automated deployment framework that maintains quality while improving efficiency
• Advance Simulation Capabilities: You’ll have the opportunity to enhance our virtual testing ecosystems, ensuring our simulation suites provide the high fidelity feedback necessary to properly understand the release.
• Collaborate at Scale: Work within a large, multi-disciplinary codebase to ensure seamless integration between planning, motion, and controls teams.
  
  

What we're looking for:

• Extensive experience building production-grade applications, tools, and pipelines using Python.
• Comfortable navigating and contributing to large, complex codebases where modularity and documentation are key.
• Proven track record of delivering software in a production environment, including familiarity with CI/CD, version control at scale, and rigorous release cycles.
• Curious about data and possess the ability to extract meaningful patterns from noisy, high-dimensional datasets.
• Passionate about a driverless future and understand the high stakes of deploying software that interacts with the physical world.
  
  

Bonus Points(not required): 

• Experience with C++ in a Linux environment.
• Familiarity with Machine Learning frameworks or Large Language Model (LLM) integration.
• Background in robotics middleware (ROS, DDS) or autonomous systems.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

We are looking for a Senior Software Engineer to develop and deliver automated workflows, intelligent tooling and insightful analytics that enables our autonomy stack to scale. You will play a pivotal role in transforming massive amounts of data into actionable engineering insights, ensuring our software releases are robust, validated, and ready for the road.

What you'll be doing:

• Develop Intelligent Tooling: You will design and implement sophisticated automation to categorize and explain complex system behaviors by leveraging advanced data modeling to accelerate solutions.
• Drive Analytics: You’ll lead the creation of high-impact performance visualization tools that translate raw metrics into clear, actionable insights regarding on-road performance.
• Streamline Release Engineering: Your expertise will help the team evolve our release processes, moving toward a highly automated deployment framework that maintains quality while improving efficiency
• Advance Simulation Capabilities: You’ll have the opportunity to enhance our virtual testing ecosystems, ensuring our simulation suites provide the high fidelity feedback necessary to properly understand the release.
• Collaborate at Scale: Work within a large, multi-disciplinary codebase to ensure seamless integration between planning, motion, and controls teams.
  
  

What we're looking for:

• Extensive experience building production-grade applications, tools, and pipelines using Python.
• Comfortable navigating and contributing to large, complex codebases where modularity and documentation are key.
• Proven track record of delivering software in a production environment, including familiarity with CI/CD, version control at scale, and rigorous release cycles.
• Curious about data and possess the ability to extract meaningful patterns from noisy, high-dimensional datasets.
• Passionate about a driverless future and understand the high stakes of deploying software that interacts with the physical world.
  
  

Bonus Points(not required): 

• Experience with C++ in a Linux environment.
• Familiarity with Machine Learning frameworks or Large Language Model (LLM) integration.
• Background in robotics middleware (ROS, DDS) or autonomous systems.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

We are looking for a Senior Software Engineer to develop and deliver automated workflows, intelligent tooling and insightful analytics that enables our autonomy stack to scale. You will play a pivotal role in transforming massive amounts of data into actionable engineering insights, ensuring our software releases are robust, validated, and ready for the road.

What you'll be doing:

• Develop Intelligent Tooling: You will design and implement sophisticated automation to categorize and explain complex system behaviors by leveraging advanced data modeling to accelerate solutions.
• Drive Analytics: You’ll lead the creation of high-impact performance visualization tools that translate raw metrics into clear, actionable insights regarding on-road performance.
• Streamline Release Engineering: Your expertise will help the team evolve our release processes, moving toward a highly automated deployment framework that maintains quality while improving efficiency
• Advance Simulation Capabilities: You’ll have the opportunity to enhance our virtual testing ecosystems, ensuring our simulation suites provide the high fidelity feedback necessary to properly understand the release.
• Collaborate at Scale: Work within a large, multi-disciplinary codebase to ensure seamless integration between planning, motion, and controls teams.
  
  

What we're looking for:

• Extensive experience building production-grade applications, tools, and pipelines using Python.
• Comfortable navigating and contributing to large, complex codebases where modularity and documentation are key.
• Proven track record of delivering software in a production environment, including familiarity with CI/CD, version control at scale, and rigorous release cycles.
• Curious about data and possess the ability to extract meaningful patterns from noisy, high-dimensional datasets.
• Passionate about a driverless future and understand the high stakes of deploying software that interacts with the physical world.
  
  

Bonus Points(not required): 

• Experience with C++ in a Linux environment.
• Familiarity with Machine Learning frameworks or Large Language Model (LLM) integration.
• Background in robotics middleware (ROS, DDS) or autonomous systems.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

We are seeking a capable and highly motivated Engineer for our Developer Platform team to enable rapid and safe software delivery by providing a unified, efficient and reliable platform for all developers at Motional. You will join a talented team of engineers that focuses on improving the engineering experience and developer productivity of teams in Motional by providing them with the necessary tools and processes. You will collaborate closely with teams across the organization to identify pain points and drive initiatives that empower teams to deliver on Motional’s mission.

Your main focus will be on our real-time data pipeline which enables our AVs to communicate with our cloud services such as Command Center, Over-The-Air update systems, and ride hailing partners integration.

This is a remote-first position based anywhere in the U.S. 

What you'll do:

• Design, build, document, test, and maintain network, compute, and storage infrastructure of our cloud platform for the AV fleet.
• Monitor, support, and resolve issues as they arise in production.
• Assist with high-level analysis, system design, and code reviews.
• Communicate with stakeholders and team leads to identify system requirements.

What you'll be working on:

• Observability systems, CI/CD pipelines for integrations with ride hailing partners like Uber and Lyft.
• Cost Optimizations, Performance Optimizations, Security Improvements of Cloud Systems.
• Automation, services and LLM-integrated bots to reduce developer toil.

What you bring:

• 2+ years of professional work experience as a Software Engineer/DevOps in designing and building distributed systems.
• Experience with Golang / Python.
• Experience with AWS (EC2, EKS, ECS, Lambda, VPC, S3, IAM, IOT).
• Knowledge of CI/CD tools (e.g. GitLab CI, Jenkins).
• Experience with Infrastructure as Code (Terraform).
• Comfortable taking projects from ideation through to completion, with strong communication and collaboration across multiple international teams.
• Comfortable working in a fast-paced, continuous delivery environment.

Bonus points for experience with:

• Working with a globally distributed team.
• Experience with serverless architectures, building systems using SQL and NoSQL databases.
• Experience with time-series databases and monitoring tools (Prometheus, VictoriaMetrics, Grafana, Redash).
• Experience with GenAI solutions (Q Developer/Business, Gemini).
• Experience in Site Reliability Engineering.
• Experience with error monitoring software (Sentry, Incident.io).

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Senior Data Product Manager

AcuityMD is a software and data platform that accelerates access to medical technologies. We help MedTech companies understand how their products are used, why customers vary, and identify opportunities for physicians to better serve their patients. Each year, the FDA approves ~6,000 new medical devices. Our solution helps MedTech companies get these products to physicians more effectively so they can improve patient care with the latest technology. We're backed by Benchmark, Redpoint, ICONIQ Growth, and Ajax Health. We're a high-growth AI and Data company scaling rapidly.

As a Senior Data Product Manager, you will build and refine AcuityMD’s data assets, products, model and platform. You’ll play a critical role in identifying market opportunities, crafting compelling product offerings, and bringing agentic AI solutions to life. You will build empathy for our users, solve complex problems and drive business success for our customers and for AcuityMD. You will report to the Chief Product Officer, and work cross-functionally with engineering and customer-facing teams.

Team Mission

We are building a world-class data products team to meticulously craft and deliver data assets and algorithms that enable new workflows and power AcuityAI to solve customer problems. Our team is the critical link between what our engineers build and what our customers need. We are looking for product leaders to develop deep empathy for our users, apply data and AI to prioritize user needs, and bring new capabilities to life by aligning and coordinating across technical and customer-facing teams.

Responsibilities

• Own a product area end-to-end: deeply understand customer problems, define the product vision, clearly articulate desired outcomes and acceptance criteria, prioritize ruthlessly, and ship thoughtfully.
• Prototype, test and deliver new data products and algorithms with engineering and small, nimble cross-functional teams
• Drive discovery by partnering with customers, product marketing, and data teams to uncover unmet needs and turn them into compelling, scalable software
• Integrate AI effectively, both in how you work (e.g., research, analysis, content generation) and in what we build, leveraging LLMs and automation where they create user value and differentiation.
• Collaborate with data, machine learning, and analytics engineers to operationalize and harden our data products and tooling, and to ensure the success of solutions that build on our data
• Communicate clearly and regularly - both in meetings and in writing. You’ll drive a compelling roadmap to ensure everyone is working toward clear goals and outcomes.
• Balance short- and long-term bets. Keep the team focused on measurable outcomes today while building toward a compelling long-term product vision.
• Own key metrics to measure and improve product and business outcomes
• Uphold high standards of integrity, curiosity, and ambition—championing what’s right for our users, our team, and the business.

Your Profile

• Motivated by building exceptional products and building a winning AI company.
• 5+ years of product management experience, ideally in vertical B2B. You’ll have a successful record of managing and shipping great software products.
• 1+ years leading and building AI solutions.
• You have built complex data products as part of a cross-functional team.
• You have practical hands-on technical experience and use it extensively. You’re not afraid to get your hands dirty digging into data, building code with AI tools and tracking metrics.
• Strong grasp of product fundamentals: discovery, prioritization, metrics, user feedback loops, and go-to-market alignment. You know what good product management looks like.
• Adept at using AI tools in your daily work and experience delivering AI powered features. You have a realistic understanding of LLM capabilities and limits.
• Proven ability to make smart tradeoffs in ambiguous environments—handling technical constraints, business priorities and strong personalities.
• You pay strong attention to detail and you write things down. You communicate clearly and concisely, and tell stories using data.
• You are curious and seek to understand, question, and improve our products. You build empathy for customer problems and have a bias towards action to solve them.
• You seek hard problems to solve and are comfortable with ambiguity. You drive prioritization and make tradeoff decisions

Nice to Haves

• You have built healthcare-specific data products as part of a cross-functional team
• Prior product management experience in vertical growth stage AI solutions
• Experience with LLM integration, data-driven solutions, or complex user roles/personas.
• Ability to build prototypes with tools like Claude Code
• Deep desire to understand and empathize with a dataset before using it
• Demonstrated mentorship or leadership in previous PM roles

AcuityMD is committed to providing highly competitive cash compensation, equity, and benefits. The compensation offered for this role will be based on multiple factors such as location, the role’s scope and complexity, and the candidate’s experience and expertise, market data and may vary from the range provided. Base salary range: $175,000-$215,000

You must have an eligible work permit in the USA to be considered for this position

We Offer:

• Ground floor opportunity: Join a high-growth startup, backed by world-class investors across Enterprise SaaS and Medical Devices (Benchmark, Redpoint Ventures, and Ajax Health).
• Learning Budget: Reimbursements for relevant learning and up-skilling opportunities.
• Remote work: AcuityMD is committed to supporting full-remote flexibility for employees in the US. We provide a work-from-home stipend for all employees.
• Flexible PTO: Generous time off and flexible hours give you the freedom to do your best work.
• Paid Health, Dental, and Vision Plans: We offer 100% paid health, dental, and vision plans for all employees and 75% paid for our employees' dependents.
• Home Office Stipend: $1,000 to invest in remote office equipment and WiFi reimbursement.
• Optional Team Retreats: We meet in-person multiple times per year for co-working and social gatherings.
• Parental Leave: 8-16 weeks of fully-paid, flexible parental leave.

Who We Are:

The Company: We are builders, who are inspired by our mission to expand patient access to cutting-edge medical technologies. We value working collaboratively to solve hard problems for our customers with simple, innovative solutions. We push ourselves to learn with empathy. We foster an active culture of mentorship and inclusion, and we welcome new team members that share our values.

We're backed by Benchmark, Redpoint Ventures, Ajax Health, and several other leading software and medical device investors. Since Acuity launched in 2020, we've brought on customers ranging from publicly traded Fortune 500 companies to innovative growth-stage companies and regional medical device distributors.

The Product: AcuityMD uses data and software to help teams collaborate around the complex relationships they have with the users of medical technologies: doctors. Our platform empowers medical technology companies to see how their products are used, understand why outcomes vary, and identify opportunities where physicians or sites of care can better serve their patients.

AcuityMD is an Equal Opportunity Employer

AcuityMD is seeking to create a diverse work environment because all teams are stronger with different perspectives and life experiences. We strongly encourage people of all backgrounds to apply. We do not discriminate on the basis of race, gender, religion, color, national origin, sexual orientation, age, marital status, veteran status, or disability status. All employees and contractors of AcuityMD are responsible for maintaining a work culture free from discrimination and harassment by treating others with kindness and respect.

The Opportunity

If you've worked alongside hardware teams, you know the damage that results from a missed change request or critical context that was never relayed to the right person. Reflow exists to close that gap. We're building the first AI-powered platform built for hardware product development, one that listens across the tools teams already use, maintains a structured picture of every program, and proactively coordinates across disciplines when things inevitably change.

This is an early role for a hands-on AI engineer to design and build the agents at the core of our platform, backed by a parent company with deep roots in engineering and manufacturing. You'll own the AI systems that set our product apart: agents that understand hardware workflows, anticipate problems, and take action on behalf of engineering teams.

Who We're Looking For

We're looking for an AI engineer who works equally well in applied research and production software. You've shipped LLM-powered agent systems to real users, you have strong intuitions about prompt engineering, tool use, and orchestration patterns, and you keep up with a field that changes fast. You're comfortable evaluating new frameworks, models, and techniques on short cycles and making pragmatic build-vs.-adopt decisions as the landscape shifts.

We currently use LangChain's DeepAgent framework for our agent orchestration, but every layer of our AI stack is open to change. The right person for this role understands how agent systems work under the hood, not just one framework's abstractions, and can adapt as better tools and approaches emerge.

This senior engineer will work alongside our engineering team and head of product to build, optimize, and operate the AI agents that give users proactive coordination, risk surfacing, status summaries, and AI-generated deliverables. The role is hands-on. You'll write code daily while contributing to AI architecture decisions and helping define how we evaluate and evolve our agent capabilities over time.

What You'll Do

Your day-to-day responsibilities will include:

• Designing, building, and iterating on LLM-powered agents that coordinate across engineering disciplines, surface project risks, and generate structured deliverables (proposals, SOWs, status reports)
• Owning the agent orchestration layer (currently LangChain DeepAgent) and continuously evaluating whether to extend, replace, or supplement it as new frameworks and patterns emerge
• Implementing robust tool-use patterns that connect agents to external systems (project management tools, CAD/PLM platforms, communication channels) via APIs and integrations
• Designing and tuning prompts, chains, and retrieval strategies to maximize agent reliability, accuracy, and usefulness across diverse hardware project contexts
• Building evaluation and observability infrastructure for agent performance, including tracing, cost tracking, latency monitoring, and automated quality benchmarks
• Developing streaming agent interfaces that surface real-time progress, reasoning transparency, and proactive alerts to end users
• Staying current with rapid advances in LLMs, agent frameworks, and related tooling, and translating that awareness into actionable recommendations for the team
• Collaborating with frontend engineers on the UX of AI-powered features and with backend engineers on data pipelines and API design
• Contributing to AI architecture decisions, code reviews, and engineering best practices

Technical Requirements

Must Have:

• 5+ years of production software engineering experience, with 2+ years focused on bringing LLM-based applications or agent systems to market
• Demonstrated proficiency using AI coding tools (Cursor, Copilot, Claude, etc.) to accelerate development
• Hands-on experience building and deploying agentic systems using frameworks such as LangChain/LangGraph, CrewAI, AutoGen, or custom orchestration
• Strong understanding of LLM fundamentals: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), context window management, and token economics
• Proficiency with Python in production environments
• Experience integrating LLM-powered features with external APIs, databases, and third-party tools
• Experience designing and operating background job / async task pipelines (Celery, RQ, Temporal, or similar) for long-running agent runs and reliable retries
• Experience building multi-agent systems with planning, delegation, and inter-agent communication patterns
• Demonstrated ability to evaluate and adopt new AI tools and frameworks quickly, with a track record of staying ahead of a fast-moving field
• Strong software engineering fundamentals: clean architecture, testing, version control, and code review practices
• Ability to balance rapid experimentation with production-grade reliability

Highly Valuable:

• Direct experience with LangChain's DeepAgent or LangGraph for multi-step agent orchestration
• Background in evaluation frameworks for LLM outputs (automated scoring, human-in-the-loop evaluation, regression testing for prompts)
• Familiarity with vector databases and embedding pipelines (Pinecone, Weaviate, pgvector, or similar)
• Experience with model serving infrastructure, fine-tuning workflows, or model selection/routing strategies
• Understanding of authentication/authorization patterns (OIDC, JWT) and secure handling of user data in LLM contexts
• Background in B2B SaaS platforms, project management tools, or technical collaboration products
• Familiarity with hardware development, engineering workflows, or project management concepts (phases, gates, dependencies, requirements traceability)
• TypeScript / React fluency, enough to pair with frontend engineers on streaming agent UIs and reasoning-transparency surfaces

What We Offer

Real Impact: The opportunity to build purpose-built tooling for an entire industry that has never had it

Customer Access: Direct exposure to hundreds of real hardware projects annually through Re:Build's engineering and manufacturing companies

Technical Growth: Hands-on work at the frontier of applied AI, solving novel agent design challenges with real-world feedback loops

Autonomy: Backed by Re:Build while operating with startup independence

Benefits: Full health/dental/vision, bonus program, generous 401K, paid time off, annual learning stipend

Equity & Growth: Participation in Re:Build's LTIP equity program and opportunity for founder equity in potential spin-out

Compensation & Location

Location: Remote-first, with preference for candidates in Boston, Seattle, Los Angeles, or other cities with Re:Build offices

Compensation: Base salary range of $143,000 to $215,000 with performance bonus and long-term incentive plan offered. Potential equity stake under independent spinout scenario.

Please note this role is restricted to US citizens or lawful permanent residents only due to exposure to export controlled information.

About Re:Build Manufacturing

Re:Build Manufacturing is a growing family of industrial and engineering businesses combining enabling technologies, operational superiority, and strategic M&A to build America's next generation industrial company. At Re:Build we deploy deep expertise in engineering, operations management, and technology to supercharge the performance of our member companies. We harness deep professional expertise and a candid, principled operating culture to drive differentiated outcomes. Ours is a fast-paced environment where individuals can stretch and be challenged to pursue their fullest potential.

Re:Build was founded to pioneer a profitable model for the revitalization of US manufacturing. We've assembled a powerful set of complementary capabilities and lines of business that enable us to pursue a wide range of end markets. Our acquired businesses are grounded in build-to-print and by-the-hour engineering and design services, and we're using their combined expertise to migrate to increasingly sophisticated program development and production, as well as the generation of our own products. Our unique set of capabilities lend themselves to highly complex systems and products, and we offer customers a range of services including product and systems design, automation, fabrication, assembly, and large volume contract manufacturing. Our customers span a wide array of industries including aerospace, defense, mobility, healthcare, pharma, biotech, clean tech, chemicals, energy, lifestyle, food production, and industrial equipment.

We want to work with people that reflect the communities in which we operate

Re:Build Manufacturing is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, marital status, parental status, cultural background, organizational level, work styles, tenure and life experiences. Or for any other reason.

Re:Build is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at accommodations.ta@ReBuildmanufacturing.com or you may call us at 617.909.6275.

About the Position

Formation Bio is hiring Product Leads to own major problem domains across our drug development platform - for example, how we evaluate and acquire drug assets, how we design and run clinical trials, or how we build regulatory submissions. You will report to the CTO. 

This is a product leadership role, but not a traditional one. You won't be writing specs for engineers to build. You'll own a business domain: set the strategy, define what to build, lead a cross-functional team through delivery, and be measured on whether the work actually changes outcomes for the drug programs and business functions you serve. Your team will include engineers, data scientists, and product managers (ranging from senior ICs to managers).

You will be embedded with our clinical, translational, regulatory, and business development leaders - understanding their problems deeply enough to identify where technology can make a material difference. That means learning what makes a drug asset attractive, what drives a trial design decision, what the FDA cares about, and where the real time and cost sinks are. You should be able to drive value in a clinical review meeting in the same way you drive value in a sprint planning session.

We are not building consumer software. We are building tools, models, and systems that make specific drug development decisions faster and better - prediction models that assess drug candidate viability, AI-driven workflows that compress diligence timelines, and platforms that let non-engineers build their own analytical tools. You will own a slice of that work and be accountable for its impact.

Responsibilities

• Own the product strategy for a defined problem domain (e.g., drug candidate evaluation, clinical trial execution, regulatory intelligence). Define what to build, what to deprioritize, and how to measure success.
• Drive adoption of AI across your domain. We use LLMs, agentic workflows, and autonomous systems extensively. You should be pushing what's possible, not waiting for the technology teams to propose it.
• Design systems that domain experts across the company can build on directly. Non-engineers are already creating their own technology - your solutions should accelerate that, not bottleneck it.
• Lead a cross-functional team spanning engineering, data science, and product. Set the roadmap and make resourcing trade-offs based on business impact.
• Work directly with our core functional teams as a strategic tech partner. Translate their problems into technology solutions - not by taking feature requests, but by understanding the domain well enough to see opportunities they may not.
• Guide the data science and engineering approach within your domain, including real-world evidence analysis, prediction pipelines, clinical strategies, and dataset evaluation. You need enough technical judgment to know when an approach is sound and when to push back.
• Maintain awareness of regulatory constraints. You don't need to be a regulatory expert, but you need to build products that work within GxP requirements and clinical data governance standards.

About You

• 10+ years of experience in tech delivery with progressively broader scope, including at least one role where you led product or technology strategy and were held accountable for business outcomes.
• Life sciences experience: You've built products in biotech, pharma, or a related industry. You understand the drug development lifecycle well enough to have informed opinions about where technology does and doesn't help. You've worked in an environment shaped by regulatory constraints and scientific uncertainty.
• Strategic product thinker: You define product direction based on business outcomes, not feature velocity. You've owned a product area where success was measured in terms the business cared about - time saved, decisions improved, cost reduced - not just user engagement or ship rate.
• AI fluency: You can evaluate AI/ML approaches, make build-vs-buy decisions on AI capabilities, and push your team to use AI where it creates real leverage. You've deployed or led the deployment of LLM-based or agentic systems in a professional context.
• Technical fluency: You can engage deeply with engineers and data scientists on approach, architecture, and trade-offs. You can evaluate whether an ML model is solving the right problem, whether a data pipeline is reliable enough, and whether an engineering approach will scale.
• Cross-functional leadership: You've led teams that included engineers, data scientists, and product managers. You know how to hire, develop, and hold people to high standards.
• Business fluency: You can discuss clinical trial design, asset valuation, regulatory strategy, and competitive dynamics with senior leaders without retreating to technical jargon. You've been in rooms where the conversation was about business decisions, not product decisions, and you contributed.
• Comfortable with ambiguity and autonomy: You are highly adaptable and curious. You've worked in early-stage companies, on transformation efforts or new initiatives where the scope was unclear and the playbook didn't exist. That energizes you rather than stalls you.
• Mission-driven: You care about getting medicines to patients faster. That's what this company exists to do, and it matters to you.

Total Compensation Range: $374,000 - $467,000

About the Position 

Formation Bio is seeking a bold and forward-thinking Vice President of Engineering to lead and transform our technical capabilities into a strategic driver of clinical and commercial success. This critical leadership role will report to the CTO and serve as a senior thought partner in defining how sophisticated engineering and computational frameworks can fundamentally reshape drug development and asset acquisition.

The ideal candidate is a biotech-native technical leader who understands that our goal is not to build traditional software products, but to develop deep technical capabilities that drive hard insights. You will partner closely with Product to translate complex scientific and operational bottlenecks into high-leverage technology solutions that accelerate workflows and deliver higher-quality insights. You will oversee the engineering behind complex data science analyses - including Real-World Evidence (RWE) platforms, large-scale information ingestion pipelines, and comprehensive modeling - that move the needle for pharma decision-making. You bring the vision and [biotech] experience to ensure our technical foundation supports clinical-stage assets and highly regulated R&D workflows with precision and speed.

Responsibilities

• Define and execute a technical vision that prioritizes high-leverage computational capabilities over traditional software features, ensuring engineering effort directly increases drug asset quality and value.
• Act as a core collaborator for the CTO and leadership team, translating complex clinical and business bottlenecks into scalable engineering solutions.
• Embed engineering resources within Business Development, Clinical, and R&D units to leverage technology to address operational needs.
• Partner with Data Science to build the engineering architecture required for complex analysis, including Real-World Evidence (RWE) platforms, asset screening frameworks, and prediction pipelines.
• Collaborate with Product to build specialized tools that streamline R&D workflows, moving the needle from manual efforts to technology-augmented insight generation.
• Develop and scale proprietary platforms for the ingestion and operationalization of external, clinical, and biological datasets.
• Ensure the delivery of GxP-validated clinical environments and automated security compliance, maintaining a regulatory-first posture without sacrificing speed.
• Deploy and scale LLM-integrated features and autonomous agents to drive multiplier efficiencies across the organization.
• Manage and mentor a specialized engineering organization, fostering a high-performance culture that values technical rigor, interdisciplinary collaboration, and mission-driven delivery.
• Refine the team structure to maintain high talent density and quality engineering in a fast-paced biotech environment.

About You 

• 10+ years of experience in software/data engineering and technical leadership, with a significant background in the Boston biotech or pharma-tech ecosystem.
• Biotech Intuition: You have likely been a VP of Engineering at 2–3 different biotechs and possess a deep understanding of the drug development lifecycle, from Discovery through Phase III.
• Proven People Leader: Experience managing and mentoring a team of 15+ engineers, with a track record of driving results in highly technical, interdisciplinary environments.
• Capability mindset: You understand that we aren't building a "user app"; we are building an engine for insights. You are passionate about the backend engineering required for RWE, data science, and trial automation.
• Engineering Rigor: Deep expertise in building data ingestion pipelines and managing complex, heterogeneous biological and clinical datasets.
• Regulatory Fluency: Experience working within GxP (GCP, GLP, GMP) environments. You know how to build technical capabilities that are "regulatory-ready" from day one.
• Strategic Partner: You are comfortable discussing risk-adjusted NPV, asset-level ROI, and clinical trial bottlenecks with CEOs, CMOs, and investors.
• Mission-Driven: Passionate about using hard engineering to solve the drug development bottleneck and bring life-changing treatments to patients faster.

Total Compensation Range: $347,000 - $435,000

About the Position

As a Senior Data Scientist at Formation Bio, you will be at the forefront of revolutionizing drug development through AI and advanced analytics. In this role, you'll lead crucial initiatives that directly impact our drug development portfolio, from developing sophisticated models for patient selection to creating AI-powered solutions for clinical trial optimization. You'll work at the intersection of computational biology, machine learning, and drug development, collaborating with cross-functional teams to translate complex biological data into actionable insights. This position offers a unique opportunity to shape the future of pharmaceutical development by leveraging cutting-edge AI technologies while making a meaningful impact on patient lives. 

Responsibilities

• Lead and execute complex data science projects that directly advance our drug development portfolio 
• Develop and implement sophisticated models for therapeutic hypothesis evaluation, including patient stratification and biomarker identification
• Design and create AI models for modernizing clinical trial evaluations, including surrogate endpoints 
• Aid in the development and training of AI agents to automate and optimize biomedical workflows 
• Collaborate cross-functionally with clinical, technical, and research teams 
• Present complex analytical findings to senior stakeholders, including executive leadership 

About You

Required Qualifications 

• PhD in computational sciences or life sciences 
• 3+ years of post-academic experience in life sciences (biotech, pharma, consulting) 
• Strong programming skills, particularly in Python 
• Extensive experience in multi-modal bioinformatics analysis 

Preferred Qualifications 

• Proven expertise in cloud computing environments, including proficiency with tabular and/or graph databases 
• Strong background in machine learning and deep learning, particularly in biological applications 
• Experience with large language models (LLM)
• Demonstrated ability to collaborate effectively with engineering teams on production systems
• Strong communication skills with proven ability to present complex technical findings to senior stakeholders 

Total Compensation Range: $170,000 - $215,000