GPU Performance Engineer

Requirements:
5+ years in systems, HPC, distributed systems, or ML infrastructure.
Hands-on experience with AI inference serving.
Strong Python; working knowledge of C++/CUDA.
Experience with multi-node GPU clusters and containerized deployments.
Benchmark-driven, performance optimization mindset.
Ability to work independently with weekly checkpoints.
Nice to Have:
Quantized inference (FP8/FP4).
KV transfer/cache-tiering projects.
Open-source contributions (vLLM, SGLang, etc. ).
Bayesian optimization or autotuning experience,

Experience
3-6 yrs