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About 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.

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.

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.
• Publications at NeurIPS, ICML, ICLR, CoRL, or RSS (or arXiv work with comparable citations).
• Demonstrated hardware or robotics interest or hands-on experience.
• Strong communication: technical blogs, talks, or clear written research.

Skills

• Self-supervised representation learning
• Predictive architectures
• Embodied control
• JEPA
• V-JEPA
• I-JEPA
• Diffusion models
• DiT
• Flow matching
• VAEs
• Sim-to-real transfer
• Inverse dynamics
• Multi-modal sensor fusion
• PyTorch
• JAX
• Distributed training
• Vision-language-action models
• SSL for visual and sensor representations
• Robotics ML
• Sensor fusion

Location

• Remote

Work Type

• Full-time

Experience Level

• Researcher

Education Level

• PhD or equivalent research experience in ML, robotics, or computer vision (not required with a strong portfolio)

About the Company

• 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.

Equal Opportunity

• We hire talented people from a wide range of backgrounds. If you're excited about a role but don't meet every bullet, we still encourage you to apply.
• Menlo Research is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic.
• Menlo provides reasonable accommodations during the application process. If you need one, please let your recruiter know.