Software Engineer- Inference Performance
San Francisco · Toronto · New York · Montreal · Seattle · Full-time · Hybrid · Posted Oct 5, 2026
At a glance
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- San Francisco · Toronto · New York · Montreal · Seattle
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 https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.
About the role
- Baseten is seeking inference performance engineers to make the world's most demanding AI workloads run faster and more efficiently.
- You will work across the stack, from the inference engine and runtime through scheduling, serving, and routing, applying techniques like prefill/decode disaggregation and speculative decoding.
Responsibilities
- Implement and productionize cutting-edge inference techniques, working deep in runtime internals.
- Profile and optimize inference end to end, from kernel launch overhead and memory layout up to request scheduling, prefill/decode disaggregation, and cache-aware routing.
- Turn performance into cost savings by improving tokens per GPU-hour, raising utilization, and giving customers and internal teams clear latency/throughput/cost tradeoffs.
- Bring up and tune new model architectures on new hardware quickly, often in the same week they're released.
- Build benchmarking frameworks that measure real-world performance across model architectures, batch sizes, sequence lengths, and hardware configurations.
- Contribute upstream to open-source inference engines (vLLM, SGLang, TensorRT-LLM), and partner closely with model, infrastructure, and customer-facing teams to ship wins.
Requirements
- Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field.
- Experience with one or more general-purpose programming languages, such as Python or C++.
- Familiarity with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching).
- Strong familiarity with ML libraries, especially PyTorch, TensorRT, or TensorRT-LLM.
- Demonstrated interest and experience in LLMs.
- Deep understanding of GPU architecture.
Preferred qualifications
- Proficiency in enhancing the performance of software systems, particularly in the context of large language models (LLMs)
- Contributed to vLLM, SGLang, TensorRT-LLM, or another inference engine.
- Worked on large-scale distributed serving: autoscaling, load balancing, multi-region or multi-cloud capacity.
- Written or optimized GPU kernels (CUDA, Triton, CUTLASS, or similar)
- Worked on quantization (FP8/FP4, AWQ, GPTQ) or speculative decoding in production.
- Deep understanding of software engineering principles and a proven track record of developing and deploying AI/ML inference solutions.
Benefits
- Competitive compensation, including meaningful equity
- 100% coverage of medical, dental, and vision insurance for employee and dependents (U.S. only)
- Flexible PTO policy including company wide Winter Break
- Paid parental leave
- Fertility and family-building stipend through Carrot
- Company-facilitated 401(k) (U.S. only)
- Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities
Skills
- Python
- C++
- PyTorch
- TensorRT
- TensorRT-LLM
- vLLM
- SGLang
- CUDA
- Triton
- CUTLASS
