lumaai

Research Scientist - World Model

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  • hybrid
  • FullTime
  • Research & AI
  • Redwood City, CA
Work arrangement
hybrid
Employment
FullTime
Stated compensation
Not specified
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Original job description

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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
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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.
Original formatting and links

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.

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