Senior GPU Inference Performance Engineer

Advanced Micro Devices, Inc (AMD) is focused on building great products that accelerate next-generation computing experiences. They are seeking a Senior GPU Inference Performance Engineer to own end-to-end performance analysis of GPU-accelerated AI inference workloads, profiling, diagnosing, and explaining performance across the full stack.

Responsibilities:

• Full-stack GPU profiling: Instrument and analyze inference workloads across AMD Instinct (ROCm, rocProfiler, Omniperf) and NVIDIA (CUDA, Nsight Systems/Compute, DCGM) GPUs. Identify bottlenecks spanning HBM bandwidth, compute utilization, kernel scheduling, memory allocation, and PCIe/Infinity Fabric data movement
• AI serving framework performance: Profile and optimize inference engines including vLLM, SGLang, and emerging serving runtimes. Understand KV-cache management, continuous batching, PagedAttention, speculative decoding, and quantization (FP8, MXFP4, INT4) effects on throughput and latency
• Competitive performance analysis: Design and execute head-to-head benchmarks (AMD vs. NVIDIA) on standardized LLM workloads. Produce clear, data-backed explanations of why performance differs — attributing gaps to specific hardware features (HBM bandwidth, compute density, interconnect topology), software maturity (kernel libraries, operator fusion, graph compilation), or configuration differences
• Multi-server inference networking: Profile and optimize distributed inference topologies including prefill-decode (PD) disaggregation, pipeline parallelism, and tensor parallelism across multi-node clusters. Analyze network-level bottlenecks using RDMA/RoCE traces, NCCL/RCCL collective profiling, and NIC-level counters (Pensando, ConnectX). Quantify the impact of network latency, bandwidth, and congestion on end-to-end inference SLAs
• GPU operator and Kubernetes stack: Profile the overhead introduced by GPU operators, device plugins, container runtimes (Docker, containerd), and Kubernetes scheduling on inference latency. Identify and resolve jitter, cold-start, and resource contention issues in production serving environments
• Tooling and automation: Build reproducible benchmarking harnesses, profiling scripts, and performance regression dashboards. Automate trace collection and analysis to support continuous performance validation across driver, firmware, and framework updates