{"id":2912242,"url":"https://alion.io/job/amd-lead-performance-and-optimization-engineer","title":"Lead Performance and Optimization Engineer","company":{"id":93,"name":"AMD","domain":"amd.com","url":"https://alion.io/company/amd","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":{"grade":"A","score":99,"open_postings":65,"ghost_share":0,"stale_share":0.031,"repost_share":0,"time_to_fill_p50_days":53,"computed_at":"2026-11-03T05:54:40Z"}},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":14},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Bash","optional":false},{"name":"Linux","optional":false},{"name":"ONNX Runtime","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false}],"status":"closed","first_seen_at":"2026-05-20T07:28:00Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-03T14:31:17Z","board_verified":false,"closed_at":"2026-11-03T14:31:17Z","days_open":167,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":164},"description":"WHAT YOU DO AT AMD CHANGES EVERYTHING\nAt AMD, our mission is to build great products that accelerate next-generation computing experiences-from AI and data centers, to PCs, gamingand embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuityand a shared passion to create something extraordinary. When you join AMD, you’lldiscover 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.\nLead Performance and OptimizationEngineer\nTHE ROLE:\nWe 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..\nTHE PERSON:\nThe 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.\nKEY RESPONSIBILITIES:\nPerformance Engineering & Optimization\nRun and optimizeML inference workloads on CPUs using EPYCoptimizedlibraries (ZenDNN), improving throughput/latency across singleinstanceand multiinstancescenarios.\nConfigure and tune NUMA, HugePages, SMT, power/performance modes, CPU isolation, scheduler settings, scaling governors, and other OS/BIOSparameters.\nDesign and validatethread/core affinity strategies for singleinstance, multiinstance, multisocket, and frameworklevelmultiinstanceexecution models.\nOptimizeworkload behaviourthrough NUMAawarelocality, thread scheduling/pinning, batch size tuning, operatorlevelparallelism, and other CPUfocusedtechniques.\nContribute to multiinstanceexecution framework development, including policies for instance partitioning, core allocation, memory distribution, and orchestration of parallel runs on large EPYC systems.\nBenchmarking & Analysis\nDevelop and run structured benchmarks across EPYC SKUs, core counts, caching/topology variations, sockets, and diverse batch sizes.\nAnalyzescaling for singleinstancevs. multiinstanceexecution, instance placement strategies, and workload isolation.\nUse perf, VTune, ftrace/tracecmd, PMU counters, flame graphs to identifybottlenecks in compute, memory, thread scheduling, or instancelevelcompetition.\nPerform rootcauseanalysis for regressions in latency, throughput, multiinstanceefficiency, memory bandwidth, and pipeline behaviour.\nML Inference Domain Knowledge\nUnderstand how ML frameworks execute models on CPU, including tensor shapes/layouts, operator behavior, threading models, kernel dispatch, scheduling strategies, and multiinstanceruntime interactions.\nInterpret how model architecture and operator composition influence performance across single and multiple concurrent inference instances.\nCollaborate with ZenDNNand kernel/ops teams to relay findings and help guide kernel/operatorlevelimprovements.\nAutomation & Tooling\nBuild automation pipelines for singleinstanceand multiinstancebenchmarking, profiling, orchestration, scaling studies, and regression detection.\nDevelop Python/Bash tooling that manages instance spawning, CPU core partitioning, memory pinning, performance data capture, reporting, and visual dashboards.\nMaintain reproducible experiment workflows for both singleinstanceand multiinstanceconfigurations.\nRequired Skills & Qualifications\nStrong understanding of CPU architecture: pipelines, caches, TLB, NUMA, SMT/HT, vector units (AVX2/AVX512/VNNI/BF16/INT8), and memory hierarchy.\n8 to 12years in performance engineering, systems optimization, or lowlevelexecution on Linux.\nHandson experience with Linux tuning and serverclassOS/BIOSconfiguration.\nProficiencywith perf, VTune, PMU counters, ftrace/tracecmd, flame graphs, and multiinstanceprofiling.\nStrong knowledge of ML inference execution (tensors, operators, threading models) on CPU backends.\nStrong Python and Bash for automation and performance tooling.\nExperience in multicore scaling, thread affinity, scheduler behavior, concurrency techniques, and multiinstanceexecution strategies.\nFamiliarity with PyTorch, TensorFlow, ONNX Runtime for running inference workloads.\n\nBenefits offered are described: AMD benefits at a glance.\nAMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.\nAMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.\n\nThis posting is for an existing vacancy.\nLead Performance and OptimizationEngineer\nTHE ROLE:\nWe 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..\nTHE PERSON:\nThe 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.\nKEY RESPONSIBILITIES:\nPerformance Engineering & Optimization\nRun and optimizeML inference workloads on CPUs using EPYCoptimizedlibraries (ZenDNN), improving throughput/latency across singleinstanceand multiinstancescenarios.\nConfigure and tune NUMA, HugePages, SMT, power/performance modes, CPU isolation, scheduler settings, scaling governors, and other OS/BIOSparameters.\nDesign and validatethread/core affinity strategies for singleinstance, multiinstance, multisocket, and frameworklevelmultiinstanceexecution models.\nOptimizeworkload behaviourthrough NUMAawarelocality, thread scheduling/pinning, batch size tuning, operatorlevelparallelism, and other CPUfocusedtechniques.\nContribute to multiinstanceexecution framework development, including policies for instance partitioning, core allocation, memory distribution, and orchestration of parallel runs on large EPYC systems.\nBenchmarking & Analysis\nDevelop and run structured benchmarks across EPYC SKUs, core counts, caching/topology variations, sockets, and diverse batch sizes.\nAnalyzescaling for singleinstancevs. multiinstanceexecution, instance placement strategies, and workload isolation.\nUse perf, VTune, ftrace/tracecmd, PMU counters, flame graphs to identifybottlenecks in compute, memory, thread scheduling, or instancelevelcompetition.\nPerform rootcauseanalysis for regressions in latency, throughput, multiinstanceefficiency, memory bandwidth, and pipeline behaviour.\nML Inference Domain Knowledge\nUnderstand how ML frameworks execute models on CPU, including tensor shapes/layouts, operator behavior, threading models, kernel dispatch, scheduling strategies, and multiinstanceruntime interactions.\nInterpret how model architecture and operator composition influence performance across single and multiple concurrent inference instances.\nCollaborate with ZenDNNand kernel/ops teams to relay findings and help guide kernel/operatorlevelimprovements.\nAutomation & Tooling\nBuild automation pipelines for singleinstanceand multiinstancebenchmarking, profiling, orchestration, scaling studies, and regression detection.\nDevelop Python/Bash tooling that manages instance spawning, CPU core partitioning, memory pinning, performance data capture, reporting, and visual dashboards.\nMaintain reproducible experiment workflows for both singleinstanceand multiinstanceconfigurations.\nRequired Skills & Qualifications\nStrong understanding of CPU architecture: pipelines, caches, TLB, NUMA, SMT/HT, vector units (AVX2/AVX512/VNNI/BF16/INT8), and memory hierarchy.\n8 to 12years in performance engineering, systems optimization, or lowlevelexecution on Linux.\nHandson experience with Linux tuning and serverclassOS/BIOSconfiguration.\nProficiencywith perf, VTune, PMU counters, ftrace/tracecmd, flame graphs, and multiinstanceprofiling.\nStrong knowledge of ML inference execution (tensors, operators, threading models) on CPU backends.\nStrong Python and Bash for automation and performance tooling.\nExperience in multicore scaling, thread affinity, scheduler behavior, concurrency techniques, and multiinstanceexecution strategies.\nFamiliarity with PyTorch, TensorFlow, ONNX Runtime for running inference workloads.\n\nBenefits offered are described: AMD benefits at a glance.\nAMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.\nAMD may use Artificial Intelligence to help screen, assess or select applicants for this position. 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