Researcher, World Models

Menlo • San Francisco, CA

6d

About The Position

Menlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable, turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.

The Role
We are hiring a Researcher to advance the world models at the core of Asimov's ability to perceive, predict, and act. You will work at the intersection of self-supervised representation learning, predictive architectures, and embodied control, in close collaboration with our platform, firmware, and hardware teams.

Requirements

• Proven modeling track record: you have 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: familiarity with prior and adjacent work, including VLA (vision-language-action) models, and a view on their trade-offs.
• Depth in a modality: strong depth in at least one sensory domain (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.
• Conversant, ideally deep, in several of: SSL for visual and sensor representations; world models (JEPA, V-JEPA, I-JEPA, LeJEPA, MJEPA); generative and predictive architectures (diffusion, DiT, flow matching, VAEs); robotics ML (VLA, inverse dynamics, sim-to-real, optical flow); sensor fusion (vision, proprioception, force/torque, multi-modal encoders); PyTorch, JAX, and distributed training.

Nice To Haves

• Publications at NeurIPS, ICML, ICLR, CoRL, or RSS (or arXiv work with comparable citations).
• 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.

Responsibilities

• Design, train, and rigorously evaluate world models that let Asimov 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: 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 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 This Job Offers

• Job Type: Full-time
• Career Level: Senior