{"schemaVersion":"jobsearcher.job.v1","id":"b2981bf3e65cd2caed68411c","url":"https://jobsearcher.com/jobs/b2981bf3e65cd2caed68411c","canonicalUrl":"https://jobsearcher.com/jobs/b2981bf3e65cd2caed68411c","title":"Machine Learning Engineer - Kernels","description":"Experience:2+ years in GPU programming, parallel computing, or systems-level optimization\r\nCore Areas:GPU Kernel Development, Machine Learning Workload Optimization, CUDA, C++, Parallel Computing, Performance Profiling, Hardware Acceleration, Distributed Computing\r\nCompensation:$150,000 – $190,000 per year\r\nAbout the RoleThis opportunity is for a Machine Learning Engineer - Kernels specializing in custom GPU and accelerator kernel development for high-performance AI and machine learning workloads. The role focuses on designing efficient low-level implementations, optimizing computational performance, and translating advances in machine learning algorithms into reliable, production-ready code.\r\nThe position combines GPU programming, parallel computing, hardware-aware optimization, and machine learning infrastructure. Work involves C++, CUDA, performance profiling, benchmarking, and optimization across distributed and heterogeneous computing environments. The engineer will collaborate with researchers, evaluate hardware developments involving CUDA, ROCm, and TPUs, and contribute to engineering practices that improve computational efficiency for advanced AI workloads.\r\nWhat You'll DoDesign, develop, and implement custom GPU and accelerator kernels to maximize computational performance for machine learning workloads.\r\nProfile and benchmark performance-critical ML workloads, identify computational bottlenecks, and implement low-level optimizations to improve execution efficiency.\r\nCollaborate with machine learning researchers to translate algorithmic advances into efficient, production-ready software implementations.\r\nEvaluate developments in accelerator hardware and programming technologies, including CUDA, ROCm, and TPUs, to guide kernel design and optimization decisions.\r\nDocument low-level optimization techniques and share performance engineering best practices with technical teams.\r\nQualificationsRequired Experience\r\nAt least 2 years of professional experience in GPU programming, parallel computing, or systems-level performance optimization.\r\nExperience optimizing computational workloads for distributed and heterogeneous computing environments.\r\nRequired Skills\r\nStrong programming skills in C++, CUDA, or comparable low-level programming languages used for performance-intensive computing.\r\nFamiliarity with machine learning frameworks and their low-level computational backends.\r\nAbility to use profiling tools and performance diagnostics to investigate execution behavior and identify optimization opportunities.\r\nStrong understanding of performance-oriented programming and the interaction between algorithms and hardware.\r\nDetail-oriented approach to engineering, with a strong focus on computational efficiency and performance optimization.\r\nAbility to collaborate effectively on research-driven engineering challenges and contribute to technically ambitious development work.\r\nEducation\r\nBachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.#J-18808-Ljbffr","company":"7seventy","rawCompany":"7seventy","city":"Brooklyn","state":"NY","isRemote":false,"isActive":false,"createdAt":"2026-10-14T01:32:45.858Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"334111","title":"Electronic Computer Manufacturing","slug":"electronic-computer-manufacturing"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer - Kernels","description":"Experience:2+ years in GPU programming, parallel computing, or systems-level optimization\r\nCore Areas:GPU Kernel Development, Machine Learning Workload Optimization, CUDA, C++, Parallel Computing, Performance Profiling, Hardware Acceleration, Distributed Computing\r\nCompensation:$150,000 – $190,000 per year\r\nAbout the RoleThis opportunity is for a Machine Learning Engineer - Kernels specializing in custom GPU and accelerator kernel development for high-performance AI and machine learning workloads. The role focuses on designing efficient low-level implementations, optimizing computational performance, and translating advances in machine learning algorithms into reliable, production-ready code.\r\nThe position combines GPU programming, parallel computing, hardware-aware optimization, and machine learning infrastructure. Work involves C++, CUDA, performance profiling, benchmarking, and optimization across distributed and heterogeneous computing environments. The engineer will collaborate with researchers, evaluate hardware developments involving CUDA, ROCm, and TPUs, and contribute to engineering practices that improve computational efficiency for advanced AI workloads.\r\nWhat You'll DoDesign, develop, and implement custom GPU and accelerator kernels to maximize computational performance for machine learning workloads.\r\nProfile and benchmark performance-critical ML workloads, identify computational bottlenecks, and implement low-level optimizations to improve execution efficiency.\r\nCollaborate with machine learning researchers to translate algorithmic advances into efficient, production-ready software implementations.\r\nEvaluate developments in accelerator hardware and programming technologies, including CUDA, ROCm, and TPUs, to guide kernel design and optimization decisions.\r\nDocument low-level optimization techniques and share performance engineering best practices with technical teams.\r\nQualificationsRequired Experience\r\nAt least 2 years of professional experience in GPU programming, parallel computing, or systems-level performance optimization.\r\nExperience optimizing computational workloads for distributed and heterogeneous computing environments.\r\nRequired Skills\r\nStrong programming skills in C++, CUDA, or comparable low-level programming languages used for performance-intensive computing.\r\nFamiliarity with machine learning frameworks and their low-level computational backends.\r\nAbility to use profiling tools and performance diagnostics to investigate execution behavior and identify optimization opportunities.\r\nStrong understanding of performance-oriented programming and the interaction between algorithms and hardware.\r\nDetail-oriented approach to engineering, with a strong focus on computational efficiency and performance optimization.\r\nAbility to collaborate effectively on research-driven engineering challenges and contribute to technically ambitious development work.\r\nEducation\r\nBachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.#J-18808-Ljbffr","datePosted":"2026-10-14T01:32:45.858Z","dateModified":"2026-10-14T01:32:45.858Z","hiringOrganization":{"@type":"Organization","name":"7seventy","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Brooklyn","addressRegion":"NY","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b2981bf3e65cd2caed68411c"},"url":"https://jobsearcher.com/jobs/b2981bf3e65cd2caed68411c"}}