{"id":3796313,"url":"https://alion.io/job/amd-lead-ai-power-performance-engineer","title":"Lead AI Power & Performance 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":97,"open_postings":17,"ghost_share":0,"stale_share":0.118,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","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":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Bash","optional":false},{"name":"C++","optional":false},{"name":"Linux","optional":false},{"name":"llama.cpp","optional":false},{"name":"ONNX Runtime","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"ROCm","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"vLLM","optional":false},{"name":"Docker","optional":true},{"name":"KV Cache","optional":true},{"name":"Machine Learning","optional":true},{"name":"Vitis","optional":true}],"status":"live","first_seen_at":"2026-11-05T07:46:00Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-06T09:08:37Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"ADVANCE YOUR CAREER. ADVANCE THE WORLD.\nAt AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.\nWhether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger - technology that moves the world forward. Join us and, together, we’ll advance your career.\nLead AI Power & Performance Engineer\nRole Overview\nWe are seeking an experienced AI Power & Performance Engineer, Lead to join our AI System Power and Performance team. The candidate will lead the evaluation, benchmarking, profiling, and optimization of AI inference workloads across modern CPU, GPU, APU, NPU, and heterogeneous computing platforms.\nThis senior technical role requires deep expertise in AI workload performance, platform architecture, and system-level optimization. The candidate will independently drive benchmarking strategy, complex root-cause analysis, competitive evaluation, and technical recommendations while collaborating with engineering, product, customer, and executive stakeholders.\nKey Responsibilities\nDefine and drive the AI performance benchmarking and workload characterization strategy across CPU, GPU, APU, NPU, and heterogeneous platforms.\nBenchmark AI inference workloads using PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, and related frameworks and runtimes.\nAnalyze key performance metrics, including latency, throughput, time to first token, tokens per second, memory utilization, power consumption, and performance per watt.\nProfile workloads and identify bottlenecks across AI frameworks, runtimes, libraries, drivers, operating systems, firmware, memory subsystems, and hardware.\nLead complex root-cause analysis and recommend optimizations related to quantization, graph execution, kernels, memory usage, workload scheduling, and heterogeneous compute utilization.\nConduct competitive benchmarking across AMD and competing CPU, GPU, APU, and AI accelerator platforms.\nCollaborate with SoC architects, framework, runtime, compiler, driver, firmware, and system software teams to improve AI workload performance.\nDevelop and improve automation for benchmark execution, data collection, regression analysis, and results visualization.\nTranslate benchmarking data into actionable technical recommendations that influence software optimization, hardware design, product strategy, and customer engagements.\nPresent technical findings and recommendations to engineering teams, customers, executives, and other cross-functional stakeholders.\nProvide technical leadership, mentor engineers, and drive alignment across multiple teams.\nRequired Skills & Experience\n10 to 14+ years of relevant experience in AI performance engineering, system performance analysis, workload optimization, benchmarking, or post-silicon performance validation.\nHands-on experience evaluating, benchmarking, and characterizing AI inference workloads on CPU, GPU, APU, NPU, or heterogeneous computing platforms.\nStrong experience with PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, or equivalent AI frameworks and inference runtimes.\nDeep expertise in platform architecture, hardware performance, and system-level optimization, including CPU, GPU, NPU, memory, power, and thermal subsystems.\nStrong Linux systems expertise, including performance profiling, workload tracing, memory management, workload scheduling, automation, and system-level debugging.\nExperience with profiling tools such as Linux perf, AMD uProf, ROCm Profiler, Nsight Systems, VTune, Perfetto, or equivalent tools.\nStrong understanding of hardware architecture, memory hierarchy, data movement, power and thermal behavior, and performance trade-offs.\nExperience with AI optimization techniques such as quantization, mixed-precision inference, graph optimization, operator fusion, kernel tuning, and memory optimization.\nProficiency in Python and Bash for benchmark automation, data analysis, and reporting.\nWorking knowledge of C or C++ for performance debugging and low-level workload analysis.\nAbility to independently drive benchmarking strategy, competitive analysis, root-cause investigation, and technical recommendations.\nExcellent executive, customer-facing, and cross-functional communication skills.\nAbility to convert complex performance data into clear technical reports, dashboards, presentations, and actionable recommendations.\nProven ability to provide technical leadership and drive complex initiatives across multiple engineering teams.\nPreferred Qualifications\nExperience with ROCm, Ryzen AI Software, Vitis AI, MIGraphX, or related AI technologies.\nExperience with MLPerf Inference or comparable AI benchmarking methodologies.\nKnowledge of INT8, INT4, FP16, BF16, and mixed-precision execution.\nExperience with large language model inference, including batching, KV-cache management, model parallelism, and memory optimization.\nExperience with Docker, virtualization, and containerized AI environments.\nExposure to post-silicon bring-up, early silicon testing, competitive benchmarking, or customer-facing performance analysis.\nExperience influencing platform architecture, software roadmaps, product strategy, or customer solutions through data-driven performance analysis.\nEducation\nBachelor’s or master’s degree in Computer Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.\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 AI Power & Performance Engineer\nRole Overview\nWe are seeking an experienced AI Power & Performance Engineer, Lead to join our AI System Power and Performance team. The candidate will lead the evaluation, benchmarking, profiling, and optimization of AI inference workloads across modern CPU, GPU, APU, NPU, and heterogeneous computing platforms.\nThis senior technical role requires deep expertise in AI workload performance, platform architecture, and system-level optimization. The candidate will independently drive benchmarking strategy, complex root-cause analysis, competitive evaluation, and technical recommendations while collaborating with engineering, product, customer, and executive stakeholders.\nKey Responsibilities\nDefine and drive the AI performance benchmarking and workload characterization strategy across CPU, GPU, APU, NPU, and heterogeneous platforms.\nBenchmark AI inference workloads using PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, and related frameworks and runtimes.\nAnalyze key performance metrics, including latency, throughput, time to first token, tokens per second, memory utilization, power consumption, and performance per watt.\nProfile workloads and identify bottlenecks across AI frameworks, runtimes, libraries, drivers, operating systems, firmware, memory subsystems, and hardware.\nLead complex root-cause analysis and recommend optimizations related to quantization, graph execution, kernels, memory usage, workload scheduling, and heterogeneous compute utilization.\nConduct competitive benchmarking across AMD and competing CPU, GPU, APU, and AI accelerator platforms.\nCollaborate with SoC architects, framework, runtime, compiler, driver, firmware, and system software teams to improve AI workload performance.\nDevelop and improve automation for benchmark execution, data collection, regression analysis, and results visualization.\nTranslate benchmarking data into actionable technical recommendations that influence software optimization, hardware design, product strategy, and customer engagements.\nPresent technical findings and recommendations to engineering teams, customers, executives, and other cross-functional stakeholders.\nProvide technical leadership, mentor engineers, and drive alignment across multiple teams.\nRequired Skills & Experience\n10 to 14+ years of relevant experience in AI performance engineering, system performance analysis, workload optimization, benchmarking, or post-silicon performance validation.\nHands-on experience evaluating, benchmarking, and characterizing AI inference workloads on CPU, GPU, APU, NPU, or heterogeneous computing platforms.\nStrong experience with PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, or equivalent AI frameworks and inference runtimes.\nDeep expertise in platform architecture, hardware performance, and system-level optimization, including CPU, GPU, NPU, memory, power, and thermal subsystems.\nStrong Linux systems expertise, including performance profiling, workload tracing, memory management, workload scheduling, automation, and system-level debugging.\nExperience with profiling tools such as Linux perf, AMD uProf, ROCm Profiler, Nsight Systems, VTune, Perfetto, or equivalent tools.\nStrong understanding of hardware architecture, memory hierarchy, data movement, power and thermal behavior, and performance trade-offs.\nExperience with AI optimization techniques such as quantization, mixed-precision inference, graph optimization, operator fusion, kernel tuning, and memory optimization.\nProficiency in Python and Bash for benchmark automation, data analysis, and reporting.\nWorking knowledge of C or C++ for performance debugging and low-level workload analysis.\nAbility to independently drive benchmarking strategy, competitive analysis, root-cause investigation, and technical recommendations.\nExcellent executive, customer-facing, and cross-functional communication skills.\nAbility to convert complex performance data into clear technical reports, dashboards, presentations, and actionable recommendations.\nProven ability to provide technical leadership and drive complex initiatives across multiple engineering teams.\nPreferred Qualifications\nExperience with ROCm, Ryzen AI Software, Vitis AI, MIGraphX, or related AI technologies.\nExperience with MLPerf Inference or comparable AI benchmarking methodologies.\nKnowledge of INT8, INT4, FP16, BF16, and mixed-precision execution.\nExperience with large language model inference, including batching, KV-cache management, model parallelism, and memory optimization.\nExperience with Docker, virtualization, and containerized AI environments.\nExposure to post-silicon bring-up, early silicon testing, competitive benchmarking, or customer-facing performance analysis.\nExperience influencing platform architecture, software roadmaps, product strategy, or customer solutions through data-driven performance analysis.\nEducation\nBachelor’s or master’s degree in Computer Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.\nBenefits offered are described: AMD benefits at a glance.\nAMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. 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