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Our Infrastructure Engineering team is a systems engineering group with company-level responsibility. At Luma, reliability engineers work directly with the researchers and products pushing the limits of multimodal intelligence. We operate close to the metal: Kernels, Containers, Schedulers, Networking, Storage, GPU behavior. But we are also responsible for something bigger: Turning deep systems knowledge into repeatable, scalable reliability for the entire company. We are hiring a leader who will define that direction. You will be a technical authority, an organizational force multiplier, and a magnet for other great engineers.
Job Responsibility:
Reliability of the Frontier: Architect and operate large, heterogeneous GPU environments under extreme demand
Improve utilization and performance where small gains materially change company outcomes
Resolve failures that span hardware, OS, runtimes, and orchestration
Eliminate entire classes of instability
Build mechanisms that make heroics unnecessary
Scaling Training & Inference: Define how infrastructure and workloads evolve as cluster size and concurrency grow
Design scheduling, placement, and resource management approaches for increasingly complex jobs
Work directly with research to build the systems required for new model capabilities
Ensure inference platforms scale rapidly without sacrificing reliability or latency
Anticipate where today’s abstractions will fail and redesign ahead of them
Building the Organization: Hire and develop exceptional systems and reliability engineers
Set the bar for technical depth, judgment, and production ownership
Shape architecture early through strong partnerships with research and product
Translate reliability constraints into long-term platform strategy
Requirements:
Deep expertise in Linux and distributed systems
Experience operating GPU / accelerator clusters in real production environments
Strong fluency in Kubernetes and modern open-source infrastructure
Comfortable debugging across hardware → kernel → runtime → orchestration
You understand how systems behave under contention and at scale
You write code and build automation
You think in bottlenecks, failure modes, and tradeoffs
Engineers trust your judgment, especially when things break