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Baseten powers mission-critical inference for leading AI companies including Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. The company recently raised $1.5B in Series F funding and is building the platform engineers rely on to ship AI products.
You will lead part of the Inference Performance team, which optimizes how the world's most demanding AI workloads run on GPUs. This is a hands-on technical leadership role where you'll manage and grow a team of inference performance engineers working across the inference engine and runtime, including kernels, scheduling, batching, KV-cache management, speculative decoding, and prefill/decode disaggregation.
Key responsibilities include:
- Lead, mentor, and develop a team of inference performance engineers through 1:1s, 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 deeply involved in technical work: review designs, guide profiling and optimization efforts, and help the team reason about performance tradeoffs
- Drive productionization of inference techniques such as quantization, speculative decoding, KV-cache reuse, chunked prefill, and custom scheduling
- Translate 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 within the same week of release
- Partner with Infrastructure, Inference Platform, Kernels, Model APIs, and customer-facing teams to set priorities and coordinate launches
- Set high standards for engineering quality, benchmarking, operational excellence, and incident response
Your team's output directly affects how fast customers' models run and how efficiently they are served. The team is scaling quickly, so you'll help shape its structure as it grows.
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 solid understanding of GPU architecture and performance tradeoffs
- Familiarity with ML libraries such as PyTorch, TensorRT, or TensorRT-LLM
- Track record of driving roadmaps and shipping complex technical projects with a team
- Clear written and verbal communication, including 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 (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
- Background as a hands-on performance or systems engineer before moving into management
