- San Francisco
- 2+ years
Responsibilities
- Develop and optimize performance-critical components across GPU/accelerator kernels, ML compiler and code-generation pipelines, or framework and serving runtimes
- Identify bottlenecks across compute, memory movement, scheduling, and execution; use profiling and measurement to guide optimization
- Improve the performance, efficiency, or capabilities of ML training and inference stacks, including their kernels, runtime components, and compiler pathways
- Work close to frameworks and serving systems such as PyTorch, JAX, vLLM, and SGLang, where relevant, to translate workload needs into low-level systems improvements
- Maintain correctness and numerical reliability while making performance tradeoffs; build clear benchmarks and validation approaches