Engineering Manager - Inference Performance

Baseten Labs Inc
  • San Francisco, CA
    2 days ago

    Job Description

    ABOUT BASETEN

    Baseten powers mission-critical inference for the worlds most dynamic AI companies, like Cursor, Notion, OpenEvidence, 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. Were growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.

    THE ROLE

    Were looking for an Engineering Manager to lead part of our Inference Performance team. This team makes the worlds most demanding AI workloads run faster and more efficiently on GPUs. Youll manage and grow a team of inference performance engineers working across the inference engine and runtime: kernels, scheduling, batching, KV-cache management, speculative decoding and prefill/decode disaggregation. This is a hands-on technical leadership role. Youll set direction, unblock hard problems and earn the teams trust by going deep on GPU performance, while also hiring, developing and supporting the people doing the work. Your teams output directly affects how fast our customers models run and how efficiently we serve them. The team is scaling quickly, so youll help shape how it is structured as it grows.

    EXAMPLE INITIATIVES

    Your team will work on these types of projects as part of our Inference Runtime team:

    • Agentic inference optimization: 50-90% faster engines

    • Agentic Kernels in Production

    • Live draft model training for speculative decoding

    • The Baseten Inference Stack

    RESPONSIBILITIES

    • Lead, mentor and grow a team of inference performance engineers through regular 1:1s, clear feedback, career development and performance reviews.

    • Hire top GPU and inference engineering talent, and build a strong, collaborative team culture as the runtime team scales.

    • Own the technical roadmap and execution for runtime performance work, balancing customer needs, new model launches and long-term platform investments.

    • Stay close to the technical work. Review designs, guide profiling and optimization efforts, and help the team reason from first principles about where time and memory go.

    • Drive the productionization of inference techniques such as quantization, speculative decoding, KV-cache reuse, chunked prefill and custom scheduling.

    • Turn performance wins into measurable outcomes: tokens per GPU-hour, utilization, latency and cost.

    • Help the team bring up and tune new model architectures on new hardware quickly, often in the same week theyre released.

    • Partner with Infrastructure, Inference Platform, Kernels, Model APIs and customer-facing teams to set priorities, coordinate launches and ship wins.

    • Set high standards for engineering quality, benchmarking, operational excellence and incident response.

    REQUIREMENTS

    • Bachelors, Masters, or Ph.D. degree in Computer Science, Engineering, Mathematics, or a related field.

    • Experience managing engineers, including hiring, mentoring, giving feedback and running performance reviews.

    • Experience leading or closely supporting GPU optimization teams in training, inference or recommendation systems.

    • Strong technical depth in GPU workloads, with a solid understanding of GPU architecture and performance tradeoffs.

    • Familiarity with ML libraries such as PyTorch, TensorRT or TensorRT-LLM.

    • A track record of driving roadmaps and shipping complex technical projects with a team.

    • Clear written and verbal communication, including the ability to align stakeholders across teams.

    NICE TO HAVE

    • Familiarity with inference engines such as vLLM, SGLang or TensorRT-LLM.

    • Experience with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching) in production.

    • Experience with GPU kernels (CUDA, Triton, CUTLASS, or similar).

    • Experience scaling a team through rapid growth at a startup.

    • A background as a hands-on performance or systems engineer before moving into management.

    BENEFITS

    • Competitive compensation, including meaningful equity

    • (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents

    • Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Years Day!)

    • Paid parental leave

    • Fertility and family-building stipend through Carrot

    • (U.S. only) Company-facilitated 401(k)

    • Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.

    Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.

    At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.

    We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

    Numbers & Facts

    LocationSan Francisco, CA

    Skills

    • Application Programming Interface (API)unmatched
    • Artificial Intelligence (AI)unmatched
    • Benchmarkingunmatched
    • CUDA (Compute Unified Device Architecture)unmatched
    • Career Developmentunmatched
    • Computer Scienceunmatched
    • Corporate Policiesunmatched
    • Customer Relationsunmatched
    • Engineering Managementunmatched
    • Establish Prioritiesunmatched
    • GPU (Graphics Processing Unit)unmatched
    • Hardware Architectureunmatched
    • Incident Responseunmatched
    • Inference Engineunmatched
    • Kernel Programmingunmatched
    • Leadershipunmatched
    • Legalunmatched
    • Machine Learningunmatched
    • Mathematicsunmatched
    • Memory Hardwareunmatched
    • Mentoringunmatched
    • Performance Engineeringunmatched
    • Performance Reviewsunmatched
    • Presentation/Verbal Skillsunmatched
    • Product Shipmentsunmatched
    • Productivity Managementunmatched
    • Programming Toolsunmatched
    • Startupunmatched
    • Systems Engineeringunmatched
    • Team Playerunmatched
    • Technical Leadershipunmatched
    • Technical Recruitingunmatched
    • Writing Skillsunmatched

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