Manager, Software Engineering

NVIDIA•Santa Clara, CA

About The Position

NVIDIA’s Managed AI Research Superclusters (MARS) team builds and scales infrastructure, platforms, and tools for researchers and engineers developing AI/ML systems. The team operates the platform NVIDIA researchers use to train frontier models, including large GPU clusters across multiple clouds and partners for pre-training, post-training, and reinforcement learning. Its systems manage workload scheduling, data and checkpoint movement, and the Kubernetes-based platform underneath. The engineering manager will lead a team responsible for a core area of the AI infrastructure platform, owning its roadmap, architecture, reliability, and on-call health. The role involves growing strong engineering teams, translating research needs into platform capabilities, shaping platform direction, and working directly with research teams and internal and external partners.

Requirements

Bachelor’s or master’s degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.
12 or more years of overall software engineering experience.
5 or more years managing engineering teams, including leading tech leads or managers.
Track record of building and operating large-scale distributed systems in production, such as 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.
Experience hiring and growing senior engineers and building teams that maintain high quality under pressure.
Experience owning reliability for critical services, including on-call, incident response, postmortems, and follow-up fixes.
Clear written communication skills, including the ability to guide contested technical questions across teams to a decision and document it.

Nice To Haves

Experience with GPU clusters for AI training at scale, including thousands of GPUs, large training runs, and high-speed interconnects.
Experience with reinforcement learning or post-training workloads and their infrastructure needs.
Contributions to open-source infrastructure projects or experience taking an internal platform open source.
Experience leading teams across time zones.
Regular use of AI coding and operations tools, with experience improving team productivity through them.

Responsibilities

Lead, hire, and grow a team of software and systems engineers, including senior and staff-level engineers and tech leads.
Own a core part of the AI infrastructure platform end to end, including its roadmap, architecture, reliability, and on-call health.
Work directly with model training teams to turn research needs into platform capabilities.
Set clear ownership and decision rights for the team’s area, including named owners, written decisions, and clear interfaces with partner teams.
Drive engineering quality at fleet scale through automation, built-in observability, and safe rollouts across many clusters.
Partner with infrastructure, storage, networking, Kubernetes, and cloud-provider teams across NVIDIA, as well as external vendors and open-source communities.
Measure reliability, latency, and other factors that matter to researchers, and use them to set priorities.
Contribute to the shared Kubernetes-based platform codebase, including its open-source path, alongside the team’s systems.