Staff AI Infrastructure Engineer

Luma • Redwood City, CA

12d • $230,000 - $360,000

About The Position

You'll own the reliability of Luma's 10k+ GPU fleet: the scheduling, efficiency, and resilience that research and products depend on. As a Staff AI Infrastructure Engineer, you'll be a technical authority who turns deep systems knowledge into repeatable, company-wide reliability, and a leader other strong engineers want to work with. This is close-to-the-metal work — kernels, containers, schedulers, networking, storage, GPU behavior — under demand hard enough that yesterday's solutions break regularly. It's also a technical-leadership role: you'll set the bar and grow the team. If most of your experience has been inside highly abstracted internal platforms where others owned the underlying machinery, this likely isn't a match.

Requirements

• Deep expertise in Linux and distributed systems.
• Experience operating GPU or accelerator clusters in real production environments.
• Strong fluency in Kubernetes and modern open-source infrastructure.
• Comfort debugging across hardware, kernel, runtime, and orchestration, and understanding how systems behave under contention and at scale.
• You write code and build automation, and think in bottlenecks, failure modes, and trade-offs.
• Judgment engineers trust, especially when things break.

Nice To Haves

• You raise reliability standards company-wide and influence product and research architecture early.
• You build partnerships rather than ticket queues, and attract and level up strong engineers.
• Curiosity for how models use infrastructure, because improving systems expands what becomes possible.

Responsibilities

• Architect and operate large, heterogeneous GPU environments under extreme demand, improving utilization and performance where small gains change company outcomes.
• Resolve failures spanning hardware, OS, runtimes, and orchestration, and eliminate whole classes of instability.
• Define how infrastructure and workloads evolve as cluster size and concurrency grow — scheduling, placement, resource management.
• Work directly with research to build the systems new model capabilities require, and scale inference without sacrificing reliability or latency.
• Hire and develop exceptional systems and reliability engineers, and set the bar for depth, judgment, and production ownership.
• Shape product and research architecture early through strong partnerships.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed