THE ROLE
This is the role at the center of the thesis. Luma already trains the strongest generative video models in the industry; the next step is turning those models into world models — interactive, controllable, physically faithful, and useful as a substrate for embodied reasoning. As a Research Scientist on the World Models team, you'll work on the next generation of generative models that can be rolled out as worlds.
WHAT YOU'LL DO
Invent next-generation world model architectures — diffusion, transformer, autoregressive, or hybrid — with a particular focus on controllability and physical consistency.
Develop controllability mechanisms that let an agent step into the world: action conditioning, view conditioning, long-horizon rollouts.
Define and own the metrics: physical fidelity, long-horizon coherence, action-following, and downstream usefulness for policy training.
Run scaling studies that tell us where compute, data, and architecture pay off.
Publish at the frontier; contribute to the open-source release that is the long-term deliverable.
MINIMUM QUALIFICATIONS
PhD or equivalent research record in ML, computer vision, robotics, or related fields.
Deep expertise in at least one of: large-scale generative modeling (video/3D/world), self-supervised representation learning, model-based RL.
Strong PyTorch and large-scale training experience — you've trained models that hit the limits of a multi-node cluster.
A research record the field knows (top-venue publications and/or widely-used open releases).
PREFERRED
Prior work on world models, model-based RL, generative video, neural simulation, or 4D scene representations.
Experience using generative models for downstream embodied tasks (planning, control, evaluation).
Excitement about open-sourcing frontier models.