16 live vacancies in the US.
You'll build the systems that make reinforcement learning work at frontier scale — coupling policy optimization with large fleets of inference workers, agentic environments, and the reward and verification systems that turn model behavior…
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.
You'll make Luma's multimodal models fast — profiling and optimizing GPU, CPU, and accelerator code so they train efficiently and deploy at scale without sacrificing quality.
You'll build the distributed systems that train Luma's large-scale multimodal models across thousands of GPUs, so researchers can focus on innovation on top of reliable, efficient, scalable infrastructure.
Editor Los Angeles (Dream Lab) You will own how Dream Lab and Luma show up on the screens people actually watch. The social optimized cut.
POSITION OVERVIEW FDC Delivery leads Luma’ forward-deployed creative team operations across the region.
Your work will focus on developing high-throughput, large-scale data processing pipelines tailored for machine learning research and internal ML platform needs.
Luma is growing fast and doesn't always have every role posted. If you believe you can do work here that matters and don't see the right opening, this is the place to tell us why.
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.
You'll help define the simulation substrate Luma uses to train general-purpose robot policies — a faithful, controllable simulation of the world built on our generative video and 3D models.
You'll turn Luma's industry-leading generative video models into world models: interactive, controllable, physically faithful, and useful as a substrate for embodied reasoning.
You'll build the distributed systems that train Luma's large-scale multimodal models across thousands of GPUs, so researchers can focus on innovation on top of reliable, efficient, scalable infrastructure.
You'll build the systems that make reinforcement learning work at frontier scale — coupling policy optimization with large fleets of inference workers, agentic environments, and the reward and verification systems that turn model behavior…
You'll make Luma's multimodal models fast — profiling and optimizing GPU, CPU, and accelerator code so they train efficiently and deploy at scale without sacrificing quality.
You'll build and train large-scale multimodal agentic models — systems that reason, plan, code, and call tools to do complex, multi-step work over pixels. This is core research shaping how users interact with what Luma's models can do.
You'll be Luma's creative lead on the ground with major brands and agencies, turning what our models can do into production workflows they actually use.