{"id":102121,"url":"https://alion.io/job/nvidia-senior-gpu-supercomputer-scheduler-engineer","title":"Senior GPU Supercomputer Scheduler Engineer","company":{"id":6,"name":"NVIDIA","domain":"nvidia.com","url":"https://alion.io/company/nvidia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":82,"open_postings":592,"ghost_share":0.019,"stale_share":0.66,"repost_share":0.034,"time_to_fill_p50_days":33,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States","Redmond, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":152000,"max":241500,"currency":"USD","period":"year","gross":null,"usd_annual":241500},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Docker","optional":false},{"name":"HPC","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Podman","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"SLURM","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false}],"status":"live","first_seen_at":"2026-08-13T00:00:00Z","employer_posted_date":"2026-08-13","last_verified_at":"2026-10-18T01:32:53Z","board_verified":true,"closed_at":null,"days_open":66,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":66},"description":"NVIDIA is a pioneer in accelerated computing, known for inventing the GPU and driving breakthroughs in gaming, computer graphics, high-performance computing, and artificial intelligence. Our technology powers everything from generative AI to autonomous systems, and we continue to shape the future of computing through innovation and collaboration. Within this mission, our team, Managed AI Research Superclusters (MARS), builds and scales the infrastructure, platforms, and tools that enable researchers and engineers to develop the next generation of AI/ML systems. By joining us, you’ll help design solutions that power some of the world’s most advanced computing workloads.\nAs a member of the Scheduling team, you will participate in the design and implementation of groundbreaking GPU compute clusters that run demanding deep learning, high performance computing, and computationally intensive workloads. We seek engineers with deep technical expertise to identify architectural directions and new approaches for AI workload scheduling to serve many simultaneous and large multi-node GPU workloads with complex requirements and dependencies. This role offers you an excellent opportunity to deliver production grade solutions, get hands on with ground-breaking technology, and work closely with technical leaders solving some of the biggest challenges in machine learning, cloud computing, and system co-design.\nWhat you'll be doing:\nDesign and develop new scheduling features and add-on services to improve GPU compute clusters across many dimensions, such as resource usage fairness, GPU occupancy, GPU waste, application resilience, application performance and power usage.\n\nDesign and develop batch workload management and orchestration services\n\nProvide support to staff and end users to resolve batch scheduler issues\n\nBuild and improve our ecosystem around GPU-accelerated computing\n\nPerformance analysis and optimizations of deep learning workflows\n\nDevelop large scale automation solutions\n\nRoot cause analysis and suggest corrective action for problems large and small scales\n\nFinding and fixing problems before they occur\n\nWhat we need to see:\nBachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience\n\n5+ years of work experience\n\nStrong understanding of batch scheduling, preferably with experience in schedulers such as SLURM or K8s batch schedulers (Kueue, Volcano, etc.)\n\nSignificant experience in systems programming languages such as C/C++ & Go as well as scripting languages such as Python and bash\n\nEstablished experience in Linux operating system, environment and tools\n\nExperience analyzing and tuning performance for a variety of AI workloads\n\nIn-depth understating of container technologies like Docker, Singularity, Podman\n\nFlexibility/adaptability for working in a dynamic environment with different frameworks and requirements\n\nExcellent communication, interpersonal and customer collaboration skills\n\nWays to stand out from the crowd:\nKnowledge in High-performance computing\n\nOpen Source Software Contribution\n\nExperience with deep learning frameworks like PyTorch and TensorFlow\n\nPassionate about SW development processes\n\nYour base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.You will also be eligible for equity and benefits.\nApplications for this job will be accepted at least until August 17, 2026.This 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. 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