Researcher, World Models
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Researcher, World Models (Humanoid Robotics)
Location: Bay Area
Salary: $120-180k + Equity
About the Role
We're building the world models that let a humanoid robot perceive, predict and act in the real world. We're looking for a Researcher to help advance that core capability, working at the intersection of self-supervised representation learning, predictive architectures and embodied control, in close collaboration with our platform, firmware and hardware teams.
This role suits someone early in their research career, roughly a year or so in, who's ready for genuine ownership rather than a narrow, tightly scoped lane.
What You'll Do
- Design, train and rigorously evaluate world models that let the robot predict the consequences of actions across visual, proprioceptive and force/torque modalities
- Advance our self-supervised learning stack for visual and sensor representations, building on and extending the JEPA family (V-JEPA, I-JEPA and related predictive-embedding approaches)
- Prototype and benchmark generative and predictive architectures (diffusion, DiT, flow matching, VAEs) against JEPA-style objectives for embodied prediction and planning
- Own the data pipeline for your experiments end to end, including curation, tooling and scaling, without depending on a separate data-engineering team to move
- Integrate what you build with our platform, firmware and software teams so your research reaches the robot, not just the paper
- Contribute to sim-to-real transfer, inverse dynamics and multi-modal sensor fusion, and publish or open-source work where it strengthens the field and the team
What We're Looking For
- A proven modelling track record: you've trained models and can show solid, honest evaluations, not just training curves
- JEPA fluency: you understand the joint-embedding predictive approach and can reason about where it fits versus alternatives
- Breadth across approaches, including familiarity with VLA (vision-language-action) models and a view on their trade-offs
- Depth in at least one sensory modality: vision, audio, natural language or similar
- Strong data abilities: you get things done without depending on a whole data-engineering team
- Solid engineering: you can implement, integrate and ship what you build alongside platform, firmware and software teams
- A humanoid robotics background, ideally hands-on, and roughly a year into your research career
Nice to Have
- Publications at NeurIPS, ICML, ICLR, CoRL or RSS (or arXiv work with comparable traction)
- A PhD or equivalent research experience in ML, robotics or computer vision; not required with a strong portfolio
- Demonstrated hardware or robotics interest or hands-on experience
- Strong communication: technical blogs, talks or clear written research
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