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.
Compensation
The base pay range for this role is $250,000 - $450,000 per year.
