Research Scientist - World Model
Luma AI
Redwood City, CA · Posted July 24, 2026
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Where Redwood City, CA ranks
Where Redwood City, CA placed in our monthly rankings, month by month. Each month stays online at its own address.
November 2026
- #2Best cities for director and executive jobs in the West Coast32% · of 71
- #3Cities with the most hybrid jobs28% · of 100
- #3Cities with the most hybrid jobs in the West Coast28% · of 66
- #8Best all-round cities for remote workers in the West Coast68 / 100 · of 39
- #9Cities that post pay most often44% · of 100
- #10Best cities for entry-level jobs that want AI skills in the West Coast17% · of 54
Pay compared with the market
This listing does not state pay. Across 74 open research scientist listings that state pay, the middle half runs $172,500 to $299,000 and the median is $228,500.
Listed pay is what employers advertise, not what people are paid.
How AI is used in this kind of work
AI use among medical scientists, except epidemiologists is little observed so far. Measured exposure for medical scientists, except epidemiologists: 4% (little observed).
Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.
Skills this job asks for
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About the job
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 Days
One 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.
About Luma: Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe multimodality is critical for intelligence — the next step beyond language models comes from vision. Luma is an equal opportunity employer.