Description
NVIDIA's Managed AI Research Superclusters (MARS) team builds and scales infrastructure, platforms, and tools for researchers and engineers to develop next-generation AI/ML systems. As an Engineering Manager, you will lead a team of software and systems engineers, own a platform area end-to-end, and work directly with research teams.
Responsibilities
- Lead, hire, and grow a team of strong 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.
Requirements
- 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.
Nice to Have
- 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 has raised a team's productivity with them.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
- 224,000 USD – 356,500 USD for Level 3;
- 272,000 USD – 431,250 USD for Level 4.
You will also be eligible for equity and benefits.