{"schemaVersion":"jobsearcher.job.v1","id":"744bb630ca0466dbd1ec26e9","url":"https://jobsearcher.com/jobs/744bb630ca0466dbd1ec26e9","canonicalUrl":"https://jobsearcher.com/jobs/744bb630ca0466dbd1ec26e9","title":"Senior Performance Modeling Engineer","description":"Overview\n\nDo you want to help shape the future of AI infrastructure and influence the hardware-software co-design decisions that power Microsoft's next generation AI platforms?\n\nJoin the Systems Planning and Architecture (SPARC) team within Azure Hardware Systems and Infrastructure (AHSI), where we are building the foundation for the world's largest AI workloads. Our team drives model enablement, performance optimization, and hardware-software co-design across Microsoft's custom AI accelerators and cloud-scale AI infrastructure.\n\nWe are seeking a Senior AI Performance Modeling Engineer to join the AI Systems Architecture (ASA) group within Microsoft SPARC, where we define and optimize next-generation AI accelerators and large-scale AI systems. In this role, you will develop analytical performance models, simulation frameworks, and workload characterization tools to evaluate accelerator and system architectures for large-scale AI training and inference.\n\nYou will model real-world AI workloads across compute, memory, and communication subsystems, identifying performance bottlenecks, scalability limits, and architectural trade-offs. A key aspect of this role is evaluating new hardware features, parallelization strategies, and system configurations to optimize performance, power efficiency (perf/W), and total cost of ownership (TCO).\n\nYou will collaborate closely with architecture, microarchitecture, compiler, runtime, and systems teams to correlate analytical predictions with silicon measurements and software traces, improve modeling accuracy, and drive data-driven architectural decisions for future AI platforms.\n\nThis role is flexible and offers a hybrid work model with three days per week in the office.\n\nMicrosoft's mission is to empower every person and every organization on the planet to achieve more. As employees, we come together with a growth mindset, innovate to empower others, and collaborate to achieve our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.\n\nResponsibilities\n\nDevelop and enhance analytical performance models and simulation frameworks for AI accelerators, GPU architectures, and large-scale AI systems.\n\nModel end-to-end AI training and inference workloads, including compute kernels, attention, MoE, memory access patterns, and communication operations.\n\nEvaluate new architectural features, compute and memory hierarchies, interconnects, and collective communication mechanisms to identify performance and efficiency improvements.\n\nPerform workload mapping and parallelization analysis across tensor, pipeline, expert, data, and context parallelism strategies to optimize accelerator utilization and scalability.\n\nDevelop system-level performance models to evaluate throughput, latency, perf/W, and TCO across accelerator, rack, and cluster configurations.\n\nCorrelate analytical model predictions with silicon measurements, kernel benchmarks, and software traces to validate assumptions, improve model fidelity, and identify performance bottlenecks.\n\nBuild and automate performance modeling, design-space exploration, sensitivity analysis, and visualization tools to accelerate architectural evaluation.\n\nCollaborate with hardware and software teams to translate modeling insights into actionable architecture recommendations and HW–SW co-design opportunities.\n\nQualifications\n\nRequired Qualifications:\n\nMaster's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 5+ years technical engineering experience OR equivalent experience\n\nOther Requirements:\n\nAbility to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:\n\nMicrosoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.\n\nPreferred Qualifications:\n\nDoctorate in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 6+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience OR equivalent experience\nFamiliarity with AI agentic systems & flow for performance modeling & anlaysis\n2+ years of experience in Computer Architecture, AI Systems, or closely related technical domains.\nStrong understanding of GPU and AI accelerator architectures, including compute pipelines, memory hierarchies, interconnects, and parallel execution models.\nExperience developing analytical performance models, roofline models, architectural simulators, or system-level performance estimation frameworks.\nKnowledge of AI training and inference workloads, including transformers, attention mechanisms, MoE architectures, and large-scale distributed execution.\nExperience modeling compute, memory bandwidth, communication collectives, and computation-communication overlap in accelerator-based systems.\nFamiliarity with parallelization strategies, including tensor, expert, pipeline, data, and context parallelism, and their performance implications.\nStrong programming skills in Python and C/C++, with experience developing modeling frameworks, simulators, and performance analysis tools.\nExperience with performance profiling, benchmarking, and correlation of analytical models with hardware measurements and software traces.\nFamiliarity with PyTorch, GPU programming, and modern AI inference or training frameworks such as vLLM, SGLang, and distributed runtime systems.\nExperience evaluating architectural design trade-offs using performance, power efficiency, scalability, and TCO metrics.\nStrong analytical, problem-solving, and communication skills, with the ability to translate performance insights into architectural recommendations.\n\nHardware Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.\n\nCertain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:\nhttps://careers.microsoft.com/us/en/us-corporate-pay\n\nThis position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.\n\nMicrosoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process.","company":"Microsoft","rawCompany":"microsoft","city":"Mountain View","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-11-11T09:30:57.178Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"17-2061.00","title":"Computer Hardware Engineers","slug":"computer-hardware-engineers"},{"code":"17-2199.00","title":"Engineers, All Other","slug":"engineers-all-other"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Performance Modeling Engineer","description":"Overview\n\nDo you want to help shape the future of AI infrastructure and influence the hardware-software co-design decisions that power Microsoft's next generation AI platforms?\n\nJoin the Systems Planning and Architecture (SPARC) team within Azure Hardware Systems and Infrastructure (AHSI), where we are building the foundation for the world's largest AI workloads. Our team drives model enablement, performance optimization, and hardware-software co-design across Microsoft's custom AI accelerators and cloud-scale AI infrastructure.\n\nWe are seeking a Senior AI Performance Modeling Engineer to join the AI Systems Architecture (ASA) group within Microsoft SPARC, where we define and optimize next-generation AI accelerators and large-scale AI systems. In this role, you will develop analytical performance models, simulation frameworks, and workload characterization tools to evaluate accelerator and system architectures for large-scale AI training and inference.\n\nYou will model real-world AI workloads across compute, memory, and communication subsystems, identifying performance bottlenecks, scalability limits, and architectural trade-offs. A key aspect of this role is evaluating new hardware features, parallelization strategies, and system configurations to optimize performance, power efficiency (perf/W), and total cost of ownership (TCO).\n\nYou will collaborate closely with architecture, microarchitecture, compiler, runtime, and systems teams to correlate analytical predictions with silicon measurements and software traces, improve modeling accuracy, and drive data-driven architectural decisions for future AI platforms.\n\nThis role is flexible and offers a hybrid work model with three days per week in the office.\n\nMicrosoft's mission is to empower every person and every organization on the planet to achieve more. As employees, we come together with a growth mindset, innovate to empower others, and collaborate to achieve our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.\n\nResponsibilities\n\nDevelop and enhance analytical performance models and simulation frameworks for AI accelerators, GPU architectures, and large-scale AI systems.\n\nModel end-to-end AI training and inference workloads, including compute kernels, attention, MoE, memory access patterns, and communication operations.\n\nEvaluate new architectural features, compute and memory hierarchies, interconnects, and collective communication mechanisms to identify performance and efficiency improvements.\n\nPerform workload mapping and parallelization analysis across tensor, pipeline, expert, data, and context parallelism strategies to optimize accelerator utilization and scalability.\n\nDevelop system-level performance models to evaluate throughput, latency, perf/W, and TCO across accelerator, rack, and cluster configurations.\n\nCorrelate analytical model predictions with silicon measurements, kernel benchmarks, and software traces to validate assumptions, improve model fidelity, and identify performance bottlenecks.\n\nBuild and automate performance modeling, design-space exploration, sensitivity analysis, and visualization tools to accelerate architectural evaluation.\n\nCollaborate with hardware and software teams to translate modeling insights into actionable architecture recommendations and HW–SW co-design opportunities.\n\nQualifications\n\nRequired Qualifications:\n\nMaster's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 5+ years technical engineering experience OR equivalent experience\n\nOther Requirements:\n\nAbility to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:\n\nMicrosoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.\n\nPreferred Qualifications:\n\nDoctorate in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 6+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience OR equivalent experience\nFamiliarity with AI agentic systems & flow for performance modeling & anlaysis\n2+ years of experience in Computer Architecture, AI Systems, or closely related technical domains.\nStrong understanding of GPU and AI accelerator architectures, including compute pipelines, memory hierarchies, interconnects, and parallel execution models.\nExperience developing analytical performance models, roofline models, architectural simulators, or system-level performance estimation frameworks.\nKnowledge of AI training and inference workloads, including transformers, attention mechanisms, MoE architectures, and large-scale distributed execution.\nExperience modeling compute, memory bandwidth, communication collectives, and computation-communication overlap in accelerator-based systems.\nFamiliarity with parallelization strategies, including tensor, expert, pipeline, data, and context parallelism, and their performance implications.\nStrong programming skills in Python and C/C++, with experience developing modeling frameworks, simulators, and performance analysis tools.\nExperience with performance profiling, benchmarking, and correlation of analytical models with hardware measurements and software traces.\nFamiliarity with PyTorch, GPU programming, and modern AI inference or training frameworks such as vLLM, SGLang, and distributed runtime systems.\nExperience evaluating architectural design trade-offs using performance, power efficiency, scalability, and TCO metrics.\nStrong analytical, problem-solving, and communication skills, with the ability to translate performance insights into architectural recommendations.\n\nHardware Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.\n\nCertain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:\nhttps://careers.microsoft.com/us/en/us-corporate-pay\n\nThis position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.\n\nMicrosoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process.","datePosted":"2026-11-11T09:30:57.178Z","dateModified":"2026-11-11T09:30:57.178Z","hiringOrganization":{"@type":"Organization","name":"Microsoft","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"744bb630ca0466dbd1ec26e9"},"url":"https://jobsearcher.com/jobs/744bb630ca0466dbd1ec26e9"}}