Member of Technical Staff, AMD GPU Kernel Engineering

The role

You will design, write, and optimize kernels for AMD GPUs in HIP, Triton, CK, and AITER. You will profile production-relevant models on AMD GPUs to remove bottlenecks through kernel optimization.

What you will do

• Design, write, and optimize kernels for AMD GPUs in HIP, Triton, CK, and AITER
• Profile production-relevant models on AMD GPUs
• Find bottlenecks and write kernels to remove them
• Provide clear correctness, latency, and throughput benchmarks
• Make AMD GPU support in vLLM usable, fast, benchmarked, and maintainable
• Write HIP, CK, or Triton attention or MoE kernels
• Implement low-latency, high-throughput sampling for large language models on AMD GPUs
• Adapt existing models for low-precision inference on AMD GPUs
• Debug kernel performance with rocprof and at the ISA level

Required qualifications

• Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar
• Hands-on experience designing and implementing GPU kernels in HIP, Triton, CUDA, CK, or a similar kernel language
• Hands-on experience optimizing AMD GPU workloads using ROCm, HIP, Triton, CK, AITER, or similar AMD ecosystem tools
• Deep understanding of AMD GPU execution, memory behavior, toolchains, kernel performance, and backend-specific performance constraints
• Experience optimizing ML kernels such as attention, GEMM, sampling, KV cache, fused kernels, or communication-heavy kernels
• Strong performance profiling and benchmarking skills

Nice to have

• Experience with vLLM, SGLang, TensorRT-LLM, ROCm-based serving, or other LLM inference systems
• Familiarity with batching, KV cache, decoding, serving tradeoffs, and backend performance constraints in production inference systems
• Experience with compiler and kernel technologies such as Triton, MLIR, LLVM, CK, AITER, HIP, or other kernel DSLs and backend libraries
• Knowledge of quantization methods such as INT8, FP8, mixed precision, or AMD hardware-specific numeric formats
• Experience building quantitative models of system performance
• Contributed to vLLM, ROCm, HIP, Triton, CK, AITER, PyTorch, compiler projects, or other open-source ML infrastructure
• Built AMD GPU benchmarking infrastructure or automated performance regression detection for accelerator workloads
• Worked directly with AMD, accelerator platform teams, or early-access programs

Benefits

• Medical coverage
• Dental coverage
• Vision coverage

Before you apply

• We sponsor visas on a case-by-case basis

About the company

Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress. Founded by the creators and core maintainers of vLLM, the company sits at the intersection of models and hardware.