Manager, Software Engineering
Manager, Software Engineering
Posted yesterday
SENIORITY
Lead
About the role
- 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 SeeBS 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 has raised a team's productivity with them.
- 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; and 272,000 USD – 431,250 USD for Level 4.You will also be eligible for equity and benefits.
- Applications for this job will be accepted at least until November 8, 2026.This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.
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