Research Scientist - World Model

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.