Research Scientist - World Model

Luma • Redwood City, CA

7d

About The Position

This role is at the center of Luma's mission to turn industry-leading generative video models into world models. These world models will be interactive, controllable, physically faithful, and useful as a substrate for embodied reasoning. The position involves inventing next-generation world-model architectures and the controllability mechanisms that allow an agent to interact within a generated world. This is a broad and open-ended research problem, suitable for a researcher with deep generative-modeling or model-based-RL expertise who has experience training models at scale.

Requirements

• 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).

Nice To Haves

• 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).
• Enthusiasm for open-sourcing frontier models.

Responsibilities

• 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 release that is the long-term deliverable.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree