Lead Performance and Optimization Engineer

WHAT YOU DO AT AMD CHANGES EVERYTHING   At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond.   Together, we advance your career.    Lead Performance and Optimization Engineer    THE ROLE:   We are seeking a Performance Engineer with strong expertise in serverclass CPUs, CPU microarchitecture, and ML inference, responsible for benchmarking, analysing, and optimizing CPU inference performance using EPYCoptimized ML libraries (e.g., ZenDNN) with common frameworks (PyTorch, TensorFlow, ONNX Runtime). The role includes handson work in performance debugging, OS/BIOS tuning, thread/core affinity, multiinstance execution, and Python/scriptingbased automation.   .       THE PERSON:   The ideal candidate should be passionate about software engineering and possess leadership skills to drive sophisticated issues to resolution. Able to communicate effectively and work optimally with different teams across AMD.

Performance Engineering & Optimization   Run and  optimize  ML inference workloads on CPUs using  EPYC optimized  libraries ( ZenDNN ), improving throughput/latency across  single instance  and  multi instance  scenarios.   Configure and tune NUMA,  HugePages , SMT, power/performance modes, CPU isolation, scheduler settings, scaling governors, and other OS/ BIOS  parameters.   Design and  validate  thread/core affinity strategies for  single instance ,  multi instance ,  multi socket , and  framework level   multi instance  execution models.   Optimize  workload  behaviour  through  NUMA aware  locality, thread scheduling/pinning, batch size tuning,  operator level  parallelism, and other  CPU focused  techniques.   Contribute to  multi instance  execution framework development, including policies for instance partitioning, core allocation, memory distribution, and orchestration of parallel runs on large EPYC systems.   Benchmarking & Analysis   Develop and run structured benchmarks across EPYC SKUs, core counts, caching/topology variations, sockets, and diverse batch sizes.   Analyze  scaling for  single instance  vs.  multi instance  execution, instance placement strategies, and workload isolation.   Use perf,  VTune ,  ftrace / trace cmd , PMU counters, flame graphs to  identify  bottlenecks in compute, memory, thread scheduling, or  instance level  competition.   Perform  root cause  analysis for regressions in latency, throughput,  multi instance  efficiency, memory bandwidth, and pipeline  behaviour .   ML Inference Domain Knowledge   Understand how ML frameworks execute models on CPU, including tensor shapes/layouts, operator  behavior , threading models, kernel dispatch, scheduling strategies, and  multi instance  runtime interactions.   Interpret how model architecture and operator composition influence performance across single and multiple concurrent inference instances.   Collaborate with  ZenDNN  and kernel/ops teams to relay findings and help guide kernel/ operator level  improvements.   Automation & Tooling   Build automation pipelines for  single instance  and  multi instance  benchmarking, profiling, orchestration, scaling studies, and regression detection.   Develop Python/Bash tooling that manages instance spawning, CPU core partitioning, memory pinning, performance data capture,