{"schemaVersion":"jobsearcher.job.v1","id":"de95e73515b9671533e769b4","url":"https://jobsearcher.com/jobs/de95e73515b9671533e769b4","canonicalUrl":"https://jobsearcher.com/jobs/de95e73515b9671533e769b4","title":"Senior Deep Learning Sofware Infrastructure Engineer","description":"NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.\nToday, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are in search of a Deep Learning Software Infrastructure Engineer to propel NVIDIA’s Autonomous Vehicles project forward. In this role, you will build and scale training libraries and infrastructure that make end-to-end autonomous driving models possible. By enabling training on thousands of GPUs and massive datasets, you will accelerate iteration speed and improve safety, working closely with research and platform teams across NVIDIA.\nWhat you’ll be doing:\nCrafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.\nImproving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.\nBuilding robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.\nCollaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.\nOwning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.\nPartnering with leadership to ensure infrastructure scales with growing GPU capacity and dataset size while maintaining developer efficiency and stability.\nWhat we need to see:\nBS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or a related field, or equivalent experience.\n12+ years of professional experience building and scaling high-performance distributed systems, ideally in ML, HPC, or large-scale data infrastructure.\nExtensive knowledge in deep learning frameworks (PyTorch is preferred), large scale training (DDP/FSDP, NCCL, tensor/pipeline parallelism), and performance profiling.\nStrong systems background: datacenter networking (RoCE, IB), parallel filesystems (Lustre), storage systems, schedulers (Slurm, Kubernetes, etc.).\nProficiency in Python with experience writing production-grade libraries, orchestration layers, and automation tools.\nAbility to work closely with multi-functional teams (ML researchers, infra engineers, product leads) and translate requirements into robust systems.\nWays to stand out from the crowd:\nShown experience scaling large GPU training clusters with >1,000 GPUs.\nExpertise in fault resilience and high availability, including elastic training and large-scale observability.\nTried leadership skills as a hands-on technical authority, encouraging others and establishing guidelines for ML systems engineering.\nYour base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.\nYou will also be eligible for equity and benefits.\nApplications for this job will be accepted at least until August 10, 2026.\nThis posting is for an existing vacancy.\nNVIDIA uses AI tools in its recruiting processes.\nNVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.","company":"NVIDIA","rawCompany":"nvidia","city":"California","state":"MD","isRemote":false,"isActive":false,"createdAt":"2026-08-09T14:43:18.340Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1244.00","title":"Network and Computer Systems Administrators","slug":"network-and-computer-systems-administrators"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"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":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Deep Learning Sofware Infrastructure Engineer","description":"NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.\nToday, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are in search of a Deep Learning Software Infrastructure Engineer to propel NVIDIA’s Autonomous Vehicles project forward. In this role, you will build and scale training libraries and infrastructure that make end-to-end autonomous driving models possible. By enabling training on thousands of GPUs and massive datasets, you will accelerate iteration speed and improve safety, working closely with research and platform teams across NVIDIA.\nWhat you’ll be doing:\nCrafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.\nImproving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.\nBuilding robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.\nCollaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.\nOwning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.\nPartnering with leadership to ensure infrastructure scales with growing GPU capacity and dataset size while maintaining developer efficiency and stability.\nWhat we need to see:\nBS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or a related field, or equivalent experience.\n12+ years of professional experience building and scaling high-performance distributed systems, ideally in ML, HPC, or large-scale data infrastructure.\nExtensive knowledge in deep learning frameworks (PyTorch is preferred), large scale training (DDP/FSDP, NCCL, tensor/pipeline parallelism), and performance profiling.\nStrong systems background: datacenter networking (RoCE, IB), parallel filesystems (Lustre), storage systems, schedulers (Slurm, Kubernetes, etc.).\nProficiency in Python with experience writing production-grade libraries, orchestration layers, and automation tools.\nAbility to work closely with multi-functional teams (ML researchers, infra engineers, product leads) and translate requirements into robust systems.\nWays to stand out from the crowd:\nShown experience scaling large GPU training clusters with >1,000 GPUs.\nExpertise in fault resilience and high availability, including elastic training and large-scale observability.\nTried leadership skills as a hands-on technical authority, encouraging others and establishing guidelines for ML systems engineering.\nYour base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.\nYou will also be eligible for equity and benefits.\nApplications for this job will be accepted at least until August 10, 2026.\nThis posting is for an existing vacancy.\nNVIDIA uses AI tools in its recruiting processes.\nNVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.","datePosted":"2026-08-09T14:43:18.340Z","dateModified":"2026-08-09T14:43:18.340Z","hiringOrganization":{"@type":"Organization","name":"NVIDIA","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"California","addressRegion":"MD","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"de95e73515b9671533e769b4"},"url":"https://jobsearcher.com/jobs/de95e73515b9671533e769b4"}}