Manager, Software Engineering

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 build solutions that power some of the most sophisticated computing workloads globally. We build and run the platform that NVIDIA's own researchers use to train frontier models. That means some of the largest GPU clusters in the world, spread across multiple clouds and partners, used for pre-training, post-training and reinforcement learning. Our engineers own how work gets scheduled onto that fleet, how data and checkpoints move through it, and the Kubernetes-based platform underneath. We are looking for an engineering manager to lead one of these engineering teams. You will grow a team of strong engineers, own a platform area end to end, and work directly with research teams whose results depend on it. You will shape what the platform becomes, not only keep it running. What you'll be doing: • Lead, hire and grow a team of software and systems engineers, including senior and staff-level engineers and tech leads. • Lead a core part of the AI infrastructure platform end to end: its roadmap, architecture, reliability and on-call health. • Turn research needs into platform capabilities by working directly with model training teams. • Set clear ownership and decision rights for your area: named owners, written decisions, and clean interfaces to partner teams. • Drive engineering quality at fleet scale: automation over manual operations, observability built in, safe rollouts across many clusters. • Partner with infrastructure, storage, networking, Kubernetes and cloud-provider teams across NVIDIA, and with external vendors and open-source communities. • Measure what matters for researchers, such as reliability, latency, and use it to set priorities. • Contribute to the shared Kubernetes-based platform codebase, including its open-source path, alongside your own team's systems. What we need to see: • BS or MS in Computer Science, Computer Engineering or a related field, or equivalent experience. • 12+ overall years in software engineering, with 5+ years managing engineering teams, including leading tech leads or managers. • A track record of building and running large-scale distributed systems in production: clusters, platforms or infrastructure services used by other engineers. • Hands-on depth in at least one of: workload scheduling and resource management, distributed storage and data systems, or Kubernetes-based platforms. • Hiring and growing senior engineers, and building teams that keep quality high under pressure. • Experience owning reliability for critical services: on-call, incident response, postmortems and the fixes that follow. • Clear written communication. You can take a contested technical question across several teams to a decision, and write it down. Ways to stand out from the crowd: • Experience with GPU clusters for AI training at scale: thousands of GPUs, large training runs, high-speed interconnects. • Experience with reinforcement learning or post-training workloads and what they need from infrastructure. • Contributions to open-source infrastructure projects, or experience taking an internal platform open source. • Has led teams across time zones. • Uses AI coding and operations tools in daily work and ha