We’re looking for an AMD GPU performance engineer to make vLLM a first-class inference engine across the AMD accelerator ecosystem. You’ll build and optimize AMD GPU backends, kernels, runtime paths, and benchmarking infrastructure using ROCm, HIP, Triton, CK, AITER, and related tooling so vLLM can deliver frontier inference performance on AMD GPUs.
You’ll work at the boundary of inference systems, kernels, compilers, and hardware architecture, improving performance-critical paths such as attention, GEMM, sampling, KV cache, and communication-heavy operations. Your work will help make AMD GPU support in vLLM usable, fast, benchmarked, and maintainable.