Engineering Manager - Forward Deployed Engineering; LLM

About Baseten

Baseten powers mission‑critical inference for the world s most dynamic AI companies, like Cursor, Notion, Open Evidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting‑edge models into production. We re growing quickly and recently raised our $300M Series E, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction.

Join us and help build the platform engineers turn to to ship AI products.

As an Engineering Manager (Player & Coach), you will lead and mentor a team of Forward Deployed Engineers focused on building, scaling, and optimizing LLM inference workloads for Baseten customers. Applying both hands‑on technical ownership and managerial leadership, you will guide your team through the processes of designing, deploying, and managing high performance, low latency AI applications on Baseten’s platform. FDE at Baseten is not a sales function – we are a mix of engineering, product, and customer architects who contribute to the core Baseten codebase, drive large portions of our feature roadmap, and execute on complicated customer engagements.

You will also partner with product, infrastructure, and other customer engineering teams to ensure that large language models (LLMs) and other generative AI systems deliver best‑in‑class performance, reliability, and cost efficiency in production environments.

• Forward Deployed Engineering on the frontier of AI
• The fastest, most accurate Whisper transcription
• Deploy production‑ready model servers from Docker images
• Deploy custom Comfy
  
  UI workflows as APIs

• Lead, mentor, and grow a team of Forward Deployed Engineers, providing guidance on technical direction, project execution, and professional development.
• Set clear goals and ensure timely, high‑quality delivery across multiple customer‑facing projects involving LLM deployment and inference optimization.
• Collaborate with leadership to align team priorities with company and customer goals, balancing short‑term delivery, widely varying customer priorities, and long‑term technical initiatives.
• Player‑coach – While much of this role will be leading the team, you will also be expected to be a key driver on strategic product initiatives and customer engagements. The best managers derive credibility from being able to be hands‑on when needed.

• Develop and maintain software systems and product features using one or more general‑purpose programming languages in a production‑level environment, with a preference for Python due to its relevance in ML projects.
• Drive customer impact by designing, implementing, and deploying Baseten solutions end‑to‑end (problem framing → evaluation → production deployment → monitoring). This involves working with customers’ engineering teams at every stage of the customer journey including: sales, implementation, and expansion.
• Deliver with velocity: turn vague objectives into clear specs and well‑defined PoCs so we can rapidly ship well‑tested services and outcomes for our customers.
• Optimize and enhance AI/ML projects, contributing to the continuous improvement of our technical stack. This includes developing features and PRDs with other engineering and product orgs.
• Own products and customer projects end‑to‑end, functioning as both an engineer, project manager, and product manager, with a focus on user empathy, project specification, and end‑to‑end execution.

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, or related field.
• 4+ years of professional software engineering experience, including 1+ year in a leadership or mentorship capacity.
• Strong programming skills in Python, with production experience in building or optimizing ML inference systems.
• Proven experience with LLMs, inference optimization, or serving frameworks (e.g., vLLM, Tensor
  
  RT, Triton, Hugging Face, Ray Serve).
• Familiarity with observability, profiling, and cost/performance tradeoffs in production ML systems.
• Excellent communication and…