# Staff AI Infrastructure Engineer - Company: [Lumaai]() - Location: Redwood City, CA - Remote: Yes - Team: Product & Engineering - Employment type: Full Time - Salary: $235K – $353K - Posted: July 24, 2026 ## Job description 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.* ## Apply [Apply on Lumaai]() Canonical job page: