You'll turn Luma's industry-leading generative video models into world models: interactive, controllable, physically faithful, and useful as a substrate for embodied reasoning. This is the role at the center of the thesis.
You'll invent next-generation world-model architectures and the controllability that lets an agent step into a generated world, and own the metrics that define success. It fits a researcher with deep generative-modeling or model-based-RL expertise who has trained models to the limits of a multi-node cluster. If you want a narrow, well-scoped research problem, this is broader and more open-ended than that.
What You'll Own- 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.
First 90 DaysOne way the first 90 could unfold.
- Days 1-30 - Immerse & Diagnose: Get deep on the current video models and where they fall short as world models.
- Days 30-60 - Ship & Validate: Prototype a controllability mechanism or architecture change and measure it against physical-fidelity and action-following metrics.
- Days 60-90 - Scale & Systemize: Run scaling studies and push the most promising direction toward the open release.
What You Bring- 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 Have- 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.
