ABOUT BASETEN
Baseten powers mission-critical inference for the world's 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. We're 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
We're looking for an Engineering Manager to lead part of our Inference Performance team. This team makes the world's most demanding AI workloads run faster and more efficiently on GPUs. You'll 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. You'll set direction, unblock hard problems and earn the team's trust by going deep on GPU performance, while also hiring, developing and supporting the people doing the work. Your team's output directly affects how fast our customers' models run and how efficiently we serve them. The team is scaling quickly, so you'll 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 they're 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
Bachelor's, Master's, 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 Year's 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).
Skills Required
- Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or a related field
- Experience managing engineers, including hiring, mentoring, giving feedback, and conducting performance reviews
- Experience leading or closely supporting GPU optimization teams in training, inference, or recommendation systems
- Strong technical understanding of GPU workloads, GPU architecture, and performance tradeoffs
- Familiarity with ML libraries such as PyTorch, TensorRT, or TensorRT-LLM
- Track record of driving technical roadmaps and shipping complex projects with a team
- Clear written and verbal communication and ability to align stakeholders across teams
- Familiarity with inference engines such as vLLM, SGLang, or TensorRT-LLM
- Production experience with LLM optimization techniques such as quantization, speculative decoding, or continuous batching
- Experience with GPU kernels, including CUDA, Triton, CUTLASS, or similar technologies
- Experience scaling a team through rapid startup growth
- Background as a hands-on performance or systems engineer before moving into management
Baseten Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Baseten and has not been reviewed or approved by Baseten.
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Fair & Transparent Compensation — Feedback suggests pay targets the top of market with explicit ranges in postings and a stated aim to provide 90th percentile salaries with equity. Role descriptions emphasize competitive, experience-based pay bands and meaningful stock grants.
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Healthcare Strength — Healthcare is described as fully covered for medical, dental, and vision for employees and their families, reducing out-of-pocket costs. This comprehensive coverage is consistently highlighted alongside other core benefits.
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Leave & Time Off Breadth — Time off policies include unlimited PTO with a minimum expectation of at least four weeks, 16 paid company holidays, and a company-wide winter break. These elements indicate substantial protected time away from work.
Baseten Insights
What We Do
AI’s future won’t be a few massive models built by a handful of labs. It’ll be millions of specialized models embedded into every product, workflow, and experience by the people closest to the customer. The foundation of that future is inference. Inference determines the performance, reliability, latency, and economics of every AI product. For AI to scale globally, it must be as reliable, fast, cost-effective, and high-quality as possible. That’s why Baseten exists. Companies like Abridge, Cursor, Lovable, Notion, and OpenEvidence depend on Baseten to power mission-critical AI workloads in production.
Why Work With Us
We’re an interdisciplinary team of researchers, engineers, and operators building the Inference Cloud our AI future demands. We’re running at a hard systems problem that requires first-principles thinking across the entire stack. The bar is high. We work hard, move fast, and care deeply about quality.
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