{"schemaVersion":"jobsearcher.job.v1","id":"a06eb4983c459951e66722d9","url":"https://jobsearcher.com/jobs/a06eb4983c459951e66722d9","canonicalUrl":"https://jobsearcher.com/jobs/a06eb4983c459951e66722d9","title":"Fellow GPU Performance Optimization Engineer","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, 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.\n\nTHE ROLE\nWe are seeking a Fellow GPU Performance Optimization Engineer to join our Models and Applications team. This role focuses on maximizing performance and efficiency of large-scale AI training workloads on AMD GPU platforms. You will drive innovations across the full software-hardware stack, optimizing distributed training at scale and pushing the limits of system throughput, scalability, and utilization for generative AI workloads.\n\nThis position requires deep expertise in GPU performance analysis, distributed systems, and ML workloads, along with the ability to influence architecture, software ecosystems, and best practices across the organization.\n\nTHE PERSON\nThe ideal candidate is a recognized technical leader with deep expertise in GPU performance optimization, large-scale distributed training, and system-level bottleneck analysis. You have a strong understanding of GPU architecture, interconnects, memory hierarchies, and communication patterns, and can translate this knowledge into measurable improvements in training efficiency at scale.\n\nYou are comfortable operating across layers—from kernels and runtimes to frameworks and distributed strategies—and have a track record of driving impactful optimizations and influencing technical direction.\n\nKEY RESPONSIBILITIES\n\nLead performance optimization of large-scale AI training workloads on AMD GPU platforms across single-node and multi-node environments.\n\nIdentify and eliminate system bottlenecks across compute, memory, and communication (e.g., kernel efficiency, memory bandwidth, network utilization).\n\nOptimize distributed training strategies (Data, Tensor, Pipeline Parallelism, ZeRO, etc.) for scalability and efficiency on AMD hardware.\n\nDrive cross-stack optimizations spanning kernels, compilers, runtimes, communication libraries, and ML frameworks.\n\nDevelop and apply advanced profiling, benchmarking, and performance modeling methodologies.\n\nCollaborate with hardware, compiler, and framework teams to influence next-generation GPU architecture and software stack design.\n\nContribute to and lead open-source efforts to improve ecosystem performance on AMD platforms.\n\nDefine best practices and guide teams on performance tuning for large-scale training workloads.\n\nStay at the forefront of advancements in large-scale training systems and performance optimization techniques.\n\nPREFERRED EXPERIENCE\n\nDeep expertise in GPU architecture and performance characteristics (compute units, memory hierarchy, interconnects such as PCIe/Infinity Fabric/RDMA).\n\nStrong experience with performance profiling tools (e.g., ROCm tools, Nsight-like systems, custom profilers) and bottleneck analysis.\n\nProven experience optimizing large-scale distributed training workloads across thousands of GPUs.\n\nExperience with distributed training frameworks such as Megatron-LM, Torchtitan, MaxText, or equivalent.\n\nStrong understanding of communication libraries and patterns (e.g., NCCL/RCCL, collective ops, overlap of compute and communication).\n\nExpertise in ML frameworks (PyTorch, JAX, TensorFlow) with a focus on performance tuning.\n\nProficiency in Python and at least one systems language (C++/CUDA/HIP), including debugging and low-level optimization.\n\nExperience with compiler stacks, kernel optimization, or graph-level optimization is a strong plus.\n\nDemonstrated technical leadership and ability to influence cross-functional teams.\n\nACADEMIC CREDENTIALS\nPh.D. in Computer Science, Computer Engineering, or a related field preferred, or equivalent industry experience with significant technical impact.\n\nLOCATION\nSan Jose, CA\n\nThis role is not eligible for visa sponsorship.\n\n#LI-MV1\n\n#HYBRID\n\nBenefits offered are described: AMD benefits at a glance.\n\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.\n\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.\n\n#J-18808-Ljbffr","company":"Careerarc","rawCompany":"careerarc","city":"San Jose","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-04-09T09:46:13.836Z","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":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"},{"code":"334111","title":"Electronic Computer Manufacturing","slug":"electronic-computer-manufacturing"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Fellow GPU Performance Optimization Engineer","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, 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.\n\nTHE ROLE\nWe are seeking a Fellow GPU Performance Optimization Engineer to join our Models and Applications team. This role focuses on maximizing performance and efficiency of large-scale AI training workloads on AMD GPU platforms. You will drive innovations across the full software-hardware stack, optimizing distributed training at scale and pushing the limits of system throughput, scalability, and utilization for generative AI workloads.\n\nThis position requires deep expertise in GPU performance analysis, distributed systems, and ML workloads, along with the ability to influence architecture, software ecosystems, and best practices across the organization.\n\nTHE PERSON\nThe ideal candidate is a recognized technical leader with deep expertise in GPU performance optimization, large-scale distributed training, and system-level bottleneck analysis. You have a strong understanding of GPU architecture, interconnects, memory hierarchies, and communication patterns, and can translate this knowledge into measurable improvements in training efficiency at scale.\n\nYou are comfortable operating across layers—from kernels and runtimes to frameworks and distributed strategies—and have a track record of driving impactful optimizations and influencing technical direction.\n\nKEY RESPONSIBILITIES\n\nLead performance optimization of large-scale AI training workloads on AMD GPU platforms across single-node and multi-node environments.\n\nIdentify and eliminate system bottlenecks across compute, memory, and communication (e.g., kernel efficiency, memory bandwidth, network utilization).\n\nOptimize distributed training strategies (Data, Tensor, Pipeline Parallelism, ZeRO, etc.) for scalability and efficiency on AMD hardware.\n\nDrive cross-stack optimizations spanning kernels, compilers, runtimes, communication libraries, and ML frameworks.\n\nDevelop and apply advanced profiling, benchmarking, and performance modeling methodologies.\n\nCollaborate with hardware, compiler, and framework teams to influence next-generation GPU architecture and software stack design.\n\nContribute to and lead open-source efforts to improve ecosystem performance on AMD platforms.\n\nDefine best practices and guide teams on performance tuning for large-scale training workloads.\n\nStay at the forefront of advancements in large-scale training systems and performance optimization techniques.\n\nPREFERRED EXPERIENCE\n\nDeep expertise in GPU architecture and performance characteristics (compute units, memory hierarchy, interconnects such as PCIe/Infinity Fabric/RDMA).\n\nStrong experience with performance profiling tools (e.g., ROCm tools, Nsight-like systems, custom profilers) and bottleneck analysis.\n\nProven experience optimizing large-scale distributed training workloads across thousands of GPUs.\n\nExperience with distributed training frameworks such as Megatron-LM, Torchtitan, MaxText, or equivalent.\n\nStrong understanding of communication libraries and patterns (e.g., NCCL/RCCL, collective ops, overlap of compute and communication).\n\nExpertise in ML frameworks (PyTorch, JAX, TensorFlow) with a focus on performance tuning.\n\nProficiency in Python and at least one systems language (C++/CUDA/HIP), including debugging and low-level optimization.\n\nExperience with compiler stacks, kernel optimization, or graph-level optimization is a strong plus.\n\nDemonstrated technical leadership and ability to influence cross-functional teams.\n\nACADEMIC CREDENTIALS\nPh.D. in Computer Science, Computer Engineering, or a related field preferred, or equivalent industry experience with significant technical impact.\n\nLOCATION\nSan Jose, CA\n\nThis role is not eligible for visa sponsorship.\n\n#LI-MV1\n\n#HYBRID\n\nBenefits offered are described: AMD benefits at a glance.\n\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.\n\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.\n\n#J-18808-Ljbffr","datePosted":"2026-04-09T09:46:13.836Z","dateModified":"2026-04-09T09:46:13.836Z","hiringOrganization":{"@type":"Organization","name":"Careerarc","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Jose","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"a06eb4983c459951e66722d9"},"url":"https://jobsearcher.com/jobs/a06eb4983c459951e66722d9"}}