You'll own how Luma's models get served — integrating new architectures into the inference engine, scaling deployments across thousands of machines, and keeping expensive GPU fleets busy while meeting internal SLOs.
This is large-scale inference systems work: scheduling, fleet management, deployment pipelines, and reliability across clusters and hardware providers.
It fits a strong systems engineer comfortable with model serving and Kubernetes at scale.
If you want pure modeling rather than the systems that run models, this is firmly the systems side.
What You'll OwnShip new model architectures by integrating them into the inference engine.
Build internal tooling to measure, profile, and track the lifetime of inference jobs and workflows.
Automate, test, and maintain inference services for maximum uptime and reliability.
Build scheduling systems that use expensive GPU resources optimally while meeting SLOs, and maintain CI/CD for model checkpoints and SDKs.
First 90 DaysOne way the first 90 could unfold.
Days 1–30 — Immerse & Diagnose: Learn the inference stack, the fleets, and where reliability or utilization break.
Days 30–60 — Ship & Validate: Integrate a model or ship tooling/scheduling that improves uptime or GPU utilization.
Days 60–90 — Scale & Systemize: Harden deployment pipelines and scheduling across clusters and providers.
What You BringStrong Python and system-architecture.