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Lead Performance and Optimization Engineer

AMD· Engineering
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BangaloreFull-timePosted 5mo ago

About the role

Performance Engineer focused on benchmarking, analyzing, and optimizing CPU inference performance for server-class CPUs using EPYC-optimized ML libraries and common frameworks, with automation, OS/BIOS tuning, and multiinstance execution experience.

Responsibilities

  • 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, multisocket, 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.
  • 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/tracecmd, 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.
  • 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.
  • 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, reporting, and visual dashboards.
  • Maintain reproducible experiment workflows for both single-instance and multi-instance configurations.

Qualifications

  • Strong understanding of CPU architecture: pipelines, caches, TLB, NUMA, SMT/HT, vector units (AVX2/AVX512/VNNI/BF16/INT8), and memory hierarchy.
  • 8 to 12 years in performance engineering, systems optimization, or low-level execution on Linux.
  • Hands-on experience with Linux tuning and server-class OS/BIOS configuration.
  • Proficiency with perf, VTune, PMU counters, ftrace/tracecmd, flame graphs, and multi-instance profiling.
  • Strong knowledge of ML inference execution (tensors, operators, threading models) on CPU backends.
  • Strong Python and Bash for automation and performance tooling.
  • Experience in multicore scaling, thread affinity, scheduler behavior, concurrency techniques, and multi-instance execution strategies.
  • Familiarity with PyTorch, TensorFlow, ONNX Runtime for running inference workloads.

Skills

Required:CPU architecture and optimizationLinux performance engineeringPerformance profiling toolsML inference frameworksPython/Shell scripting

Benefits

  • AMD benefits at a glance (link provided in posting)
Last updated Oct 22, 2026. Interested? Apply on the company site.
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