{"schemaVersion":"jobsearcher.job.v1","id":"2060dc7c3ce9d22a85c68d15","url":"https://jobsearcher.com/jobs/2060dc7c3ce9d22a85c68d15","canonicalUrl":"https://jobsearcher.com/jobs/2060dc7c3ce9d22a85c68d15","title":"Software Engineer, GPU Performance","description":"OverviewIn this role you will optimize GPU-based AI workloads at HeyGen to reduce latency and cost, collaborating with researchers and infrastructure engineers. You will profile with NVIDIA Nsight tools and PyTorch Profiler to identify bottlenecks, and implement high-performance kernels, batching strategies, and memory management. You will help bring optimizations into production while measuring latency, throughput, and output quality. This is a chance to work on cutting-edge AI video platforms and contribute to scalable, efficient model serving at scale.Compensation / Benefitscompetitive salary and benefits packagedynamic and inclusive work environmentprofessional growth and advancementcollaborative culture that values innovation and creativityaccess to the latest technologies and toolsResponsibilitiesInvestigate GPU utilization, kernel execution, memory bandwidth, and CPU–GPU data movement using NvTools and profilersIdentify bottlenecks across model execution, preprocessing, and inference serving and measure impact of optimizationsImprove performance through batching, scheduling, memory management, and better GPU utilizationDevelop or integrate high-performance GPU kernels when existing implementations limit performanceBuild benchmarks and automated checks to catch performance regressions across video workloadsCollaborate with AI researchers and infrastructure engineers to deploy optimizations into productionMeasure effects of changes on latency, throughput, cost, and output qualityKey requirementsExperience optimizing GPU-based AI workloads or HPC systemsProficiency in Python and PyTorch or similar ML frameworkStrong curiosity about GPU hardware (memory bandwidth, cache behavior, tensor cores, CPU-GPU data movement)Experience using profiling tools to connect hardware behavior to application bottlenecks and validate improvementsAbility to translate performance experiments into reliable production changes and communicate tradeoffs clearlycuriositycollaborationcommunication of complex tradeoffsCUDATritonC++ GPU programming","company":"Heygen","rawCompany":"heygen","city":"Anaheim","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-10-26T13:38:39.076Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"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"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Software Engineer, GPU Performance","description":"OverviewIn this role you will optimize GPU-based AI workloads at HeyGen to reduce latency and cost, collaborating with researchers and infrastructure engineers. You will profile with NVIDIA Nsight tools and PyTorch Profiler to identify bottlenecks, and implement high-performance kernels, batching strategies, and memory management. You will help bring optimizations into production while measuring latency, throughput, and output quality. This is a chance to work on cutting-edge AI video platforms and contribute to scalable, efficient model serving at scale.Compensation / Benefitscompetitive salary and benefits packagedynamic and inclusive work environmentprofessional growth and advancementcollaborative culture that values innovation and creativityaccess to the latest technologies and toolsResponsibilitiesInvestigate GPU utilization, kernel execution, memory bandwidth, and CPU–GPU data movement using NvTools and profilersIdentify bottlenecks across model execution, preprocessing, and inference serving and measure impact of optimizationsImprove performance through batching, scheduling, memory management, and better GPU utilizationDevelop or integrate high-performance GPU kernels when existing implementations limit performanceBuild benchmarks and automated checks to catch performance regressions across video workloadsCollaborate with AI researchers and infrastructure engineers to deploy optimizations into productionMeasure effects of changes on latency, throughput, cost, and output qualityKey requirementsExperience optimizing GPU-based AI workloads or HPC systemsProficiency in Python and PyTorch or similar ML frameworkStrong curiosity about GPU hardware (memory bandwidth, cache behavior, tensor cores, CPU-GPU data movement)Experience using profiling tools to connect hardware behavior to application bottlenecks and validate improvementsAbility to translate performance experiments into reliable production changes and communicate tradeoffs clearlycuriositycollaborationcommunication of complex tradeoffsCUDATritonC++ GPU programming","datePosted":"2026-10-26T13:38:39.076Z","dateModified":"2026-10-26T13:38:39.076Z","hiringOrganization":{"@type":"Organization","name":"Heygen","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Anaheim","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"2060dc7c3ce9d22a85c68d15"},"url":"https://jobsearcher.com/jobs/2060dc7c3ce9d22a85c68d15"}}