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.

What You'll Own

• 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.

First 90 Days

One way the first 90 could unfold.

• Days 1–30 — Immerse & Diagnose: Learn the fleet, its failure modes, and the biggest reliability and utilization gaps.

• Days 30–60 — Ship & Validate: Eliminate a recurring class of instability or land a utilization or performance win that moves company outcomes.

• Days 60–90 — Scale & Systemize: Set the reliability direction, redesign ahead of where today's abstractions will fail, and begin building the team.

What You Bring

• 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 Have

• 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.

About Luma: Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe multimodality is critical for intelligence — the next step beyond language models comes from vision. Luma is an equal opportunity employer.