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 DaysOne 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.
