Quick Summary
Key Responsibilities
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.
Requirements Summary
large-scale generative modeling (video/3D/world), self-supervised representation learning, model-based RL.
Technical Tools
Data ScientistData
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.
What We Offer
~1 min readThe base pay range for this role is $250,000 – $450,000 per year.
Location & Eligibility
Where is the job
Sf Bay Area, United States
Hybrid — some on-site time required
Who can apply
US
Listing Details
- Posted
- June 1, 2026
- First seen
- September 26, 2026
- Last seen
- September 27, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 20%
- Scored at
- September 27, 2026
Signal breakdown
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External application
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