Member of Technical Staff - ML Performance
Engineers optimize ML systems for performance at scale, focusing on GPU utilization, inference engines, and container runtime to boost throughput and reduce latency for language and diffusion models. Requires 5+ years experience with PyTorch, CUDA, and performance debugging.
About the job
Requirements
- 5+ years of experience writing high-quality, high-performance code.
- Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT).
- Familiarity with Nvidia GPU architecture and CUDA.
- Experience with ML performance engineering (tell us a story about boosting GPU performance — debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc).
Nice-to-have
- Familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).
Skills
PyTorch, vLLM, TensorRT, CUDA, Nvidia Gpu, Ml Frameworks, Linux Kernel, Containers
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