Hiring: Senior GPU Inference Performance Engineer Company: AMD Location: Santa Clara, CA Job Posted Time: 2026-09-11 18:02:29 Target Skills & Keywords : Go, HBase, LLM, Linux, containerd About the job Required Skills: •We are looking for a Senior GPU Inference Performance Engineer to own end-to-end performance analysis of GPU-accelerated AI inference workloads. You will profile, diagnose, and explain performance across the full stack from GPU silicon, communication libraries, networking fabrics, and operating systems through the software runtime and drive competitive positioning against other accelerator vendors. This role sits at the intersection of hardware, systems software, networking, and AI infrastructure, and requires someone who can go deep on a trace and present findings to product and executive stakeholders. •A hands-on performance engineer who is equally comfortable reading a GPU trace, debugging distributed systems performance issues, and briefing executives. You are curious, evidence-driven, rigorous, and you don't stop at "X is faster" and you explain why, rooted in hardware and software evidence. You collaborate across hardware, systems software, networking, and AI infrastructure teams, communicate clearly in written reports and presentations, and thrive at the intersection of silicon, operating systems, communication libraries, networking, and AI. Experience with Linux systems, distributed GPU infrastructure, RDMA/RoCE networking, or communication libraries such as NCCL/RCCL is highly valued. •Key Responsibilities •Full-stack GPU profiling: Instrument and analyze inference workloads across AMD Instinct (ROCm, rocProfiler, ROCm Systems Profiler, RGP, rocprof-compute, rocprof-sys, Omniperf) and NVIDIA (CUDA, Nsight Systems/Compute, DCGM) GPUs. Identify bottlenecks spanning HBM bandwidth, compute utilization, kernel scheduling, memory allocation, PCIe/Infinity Fabric data movement, and GPU runtime behavior. •Systems and runtime performance analysis: Profile and diagnose performance interactions between GPU runtimes, Linux operating systems, device drivers, container runtimes, memory subsystems, CPU scheduling, NUMA topology, and I/O pathways. Identify system-level bottlenecks that impact throughput, latency, and GPU utilization. •Competitive performance analysis: Design and execute head-to-head benchmarks (AMD vs. NVIDIA) on standardized AI and LLM workloads. Produce clear, data-backed explanations of why performance differs attributing gaps to hardware architecture, networking topology, communication libraries, software maturity, runtime behavior, or configuration differences. •Multi-server inference networking: Prof Compensation: •$164,000 •$246,000 Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!