Lead Infrastructure and Reliability Engineer (Systems & Scale)

Luma AI•Palo Alto, CA

About The Position

About Luma AI A new class of intelligence is emerging, systems that understand and generate the world across video, images, audio, and language. Building multimodal AGI is not just a modeling challenge. It is an infrastructure challenge at the edge of what hardware, software, and organizations can support. At Luma, we operate rapidly scaling 10k+ GPU fleets, pushing utilization, throughput, and reliability hard enough that yesterday’s solutions break regularly. Researchers depend on this infrastructure to move the frontier forward. Customers depend on it to power real creative work. Many companies run accelerators. Very few sit directly next to the teams inventing the models that redefine what those accelerators must do. At Luma, improvements to scheduling, efficiency, and reliability immediately translate into faster research iteration and entirely new product capabilities. We are still early. The playbook is still being written. A single exceptional engineer can reshape how the company operates. Where You Come In 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.

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

Responsibilities

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
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
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