Founding MTS - Dexterous Manipulation (Robot Learning)

Company Description

We scale dexterous robot policies by learning from human video.

Our team of researchers and engineers comes from Meta, Google, and CMU.

It includes a SIGGRAPH Test-of-Time Award winner, with many papers at SIGGRAPH, CVPR, ECCV, RSS and IROS.

Double-digit millions raised, led by a tier-one seed fund.

In person in Redwood City.

Role Description

Dexterous Manipulation.

You own how our robots get good with their hands, from the data we collect to the policy that runs on the robot.

You decide what to capture, how to teleoperate, and how to learn from it, and you measure success on real hardware.

You will

• Build and run our teleoperation and data collection: gloves, hand and wrist tracking, low-latency control of dexterous hands on real arms.
• Retarget human hand motion to robot hands, and decide which demonstrations are worth collecting.
• Train and post-train dexterous policies (imitation learning, diffusion or flow-matching policies, VLAs, RL fine-tuning) and deploy them on the robot.
• Close the loop: find where the policy fails on hardware, trace it back to data or model, and fix it.
• Work with our human modeling and world model teams to turn human video into robot skills.

Your first project: take one contact-rich bimanual task from first teleop demo to a policy that succeeds reliably on our real dexterous hands, with the numbers to show it.

You work with real bimanual arms, dexterous hands and teleoperation rigs.

Compute is a cluster of B300s.

Cash is $220K to $300K depending on experience, plus founding-tier equity. O-1 and H-1B sponsorship.

Qualifications

• You have trained a manipulation policy that works on a real robot, and can show the success rate.
• Hands-on with dexterous hands or high-DoF manipulation, not only parallel grippers.
• You have built or deeply used a teleoperation or data collection system, and know what makes demonstrations good or bad.
• You have carried a full loop alone: data, training, deployment, evaluation.
• You want the robot to do something hard with its hands.

Nice to have

• Gloves, motion capture, or VR teleop (MANUS, Rokoko, Vive, Quest, Apple Vision Pro).
• Tactile sensing, force control, or contact-rich in-hand manipulation.
• Human-to-robot retargeting, or learning from human video.
• Sim-to-real for dexterous hands (Isaac, MuJoCo).
• Using LLMs or agents to scale data, evaluation, or research loops. (Pure RAG or chatbot work is less relevant here.)

A PhD helps but is not required. We care most about what you have built.