Manager, Software Engineering

Details

NVIDIA's Managed AI Research Superclusters (MARS) team builds and scales the infrastructure, platforms, and tools that enable researchers and engineers to develop next-generation AI/ML systems. The team operates platforms used by NVIDIA researchers to train frontier models, including large GPU clusters across multiple clouds and partners for pre-training, post-training, and reinforcement learning.

The engineering manager will lead an engineering team responsible for a core area of the AI infrastructure platform, including workload scheduling, data and checkpoint movement, and the Kubernetes-based platform. The role owns the platform area end to end, including its roadmap, architecture, reliability, and on-call health, while working directly with research teams.

Responsibilities

• Lead, hire, and grow a team of software and systems engineers, including senior and staff-level engineers and technical leads.
• Lead a core part of the AI infrastructure platform end to end, including its roadmap, architecture, reliability, and on-call health.
• Translate research needs into platform capabilities by working directly with model training teams.
• Establish clear ownership and decision rights, including named owners, written decisions, and well-defined 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 outcomes important 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.

Requirements

• Bachelor's or master's degree in Computer Science, Computer Engineering, a related field, or equivalent experience.
• At least 12 years of overall software engineering experience.
• At least 5 years of experience managing engineering teams, including leading technical 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 expertise in at least one of the following areas:
  • Workload scheduling and resource management
  • Distributed storage and data systems
  • Kubernetes-based platforms
• Experience hiring and developing senior engineers and building teams that maintain high quality under pressure.
• Experience owning reliability for critical services, including on-call operations, incident response, postmortems, and subsequent fixes.
• Strong written communication skills, including the ability to guide contested technical questions across multiple teams to a documented decision.

Preferred Qualifications

• Experience with GPU clusters for large-scale AI training, including thousands of GPUs, large training runs, and high-speed interconnects.
• Experience with reinforcement learning or post-training workloads and their infrastructure requirements.
• Contributions to open-source infrastructure projects or experience open-sourcing an internal platform.
• Experience leading teams across time zones.
• Experience using AI coding and operations tools in daily work and improving team productivity with them.

Compensation and Benefits

The expected base salary ranges are $224,000–$356,500 for Level 3 and $272,000–$431,250 for Level 4. Base salary is determined by location, experience, and other objective job-related factors. The role is also eligible for equity and benefits.