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 turn Luma's industry-leading generative video models into world models: interactive, controllable, physically faithful, and useful as a substrate for embodied reasoning. This is the role at the center of the thesis.
What they're looking for
- PhD or equivalent research record in ML, computer vision, robotics, or a related field
- Deep expertise in at least one of: large-scale generative modeling (video/3D/world), self-supervised representation learning, or model-based RL
- Strong PyTorch and large-scale training experience, to the limits of a multi-node cluster
- A research record the field knows (top-venue publications and/or widely used open releases)
More about this role
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. This is the role at the center of the thesis.
You'll invent next-generation world-model architectures and the controllability that lets an agent step into a generated world, and own the metrics that define success. It fits a researcher with deep generative-modeling or model-based-RL expertise who has trained models to the limits of a multi-node cluster. If you want a narrow, well-scoped research problem, this is broader and more open-ended than that.
Invent next-generation world-model architectures (diffusion, transformer, autoregressive, or hybrid), focused on controllability and physical consistency.
Develop controllability mechanisms — action conditioning, view conditioning, long-horizon rollouts — that let an agent step into the world.
Define and own the metrics: physical fidelity, long-horizon coherence, action-following, and downstream usefulness for policy training.
Run scaling studies that show where compute, data, and architecture pay off.
Publish at the frontier and contribute to the open-source...
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