Luma AI is the creative AI platform for video generation and image creation. Powered by the world's leading video generation models, Ray and Uni, and creative agents handling end-to-end workflows. Trusted by leading agencies and brands. Try it free. Backed by General Catalyst, a16z and CRV.
About the role
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.
What they're looking for
- Strong Python and system-architecture skills
- Experience deploying models with PyTorch, Hugging Face, vLLM, SGLang, TensorRT-LLM, or similar
- Experience with queues, scheduling, traffic control, and fleet management at scale
- Experience with Linux, Docker, and Kubernetes, and with orchestration, deployment, and scheduling
- Familiarity with Redis and S3-compatible storage
More about this role
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.
Ship new model architectures by integrating them into the inference engine.
Collaborate across research, engineering, and infrastructure to optimize model efficiency and deployments.
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.
Manage and optimize inference workloads across clusters and hardware providers, and scale deployments across thousands of machines.
Build scheduling systems that use expensive GPU resources optimally while meeting SLOs, and maintain CI/CD for model checkpoints and SDKs.
One...
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