Research Scientist, Robot Foundation Models

About Mind:

Mind Robotics is building universally capable robots to transform industrial work, starting on the factory floor. We're vertically integrated:

• We train our own foundation models.

• We build our own robots.

• We collect data directly in the factories where our robots will work, starting with Rivian, our first manufacturing partner, in addition to third-party data.

About the team and the role:

We train robot foundation models that learn how the physical world behaves from large volumes of video and human demonstration, learn actions from teleoperation and on-robot data, and then adapt to specific stations on the line. You'll design and run that training, and you'll watch your models run on our robots in a production factory.

This is a research role with a production bar. Many of our open questions are open across the field: how much web-scale video adds on top of factory demonstrations, how tactile sensing changes what cameras need to see, and how quickly a model can adapt to a new station. You'll help answer them and ship the answers.

We're a flat organization, and everyone contributes directly to getting our robots deployed.

Responsibilities:

• Pre-train and post-train multimodal robot foundation models (vision, language, proprioception, force and tactile, actions) at large scale.

• Own experiments end to end: data mixture, training run, evaluation, and deployment on real robots.

• Shape data strategy: what to collect at our deployment sites (human demonstration, teleoperation, recovery data), how to combine it with third-party data, how to measure quality, and which mix actually moves performance.

• Build evaluation that predicts real-world success: offline metrics, simulation and on-robot trials tied to station-level task success.

• Work on the robot: debug failure modes on the line and turn model outputs into smooth, reliable actions.

• Help set our research agenda and write up what you learn.

• Build whatever you need to succeed, including your own data and training infrastructure. You'll have strong colleagues to lean on, but you own the problem end to end.

Requirements:

• You've designed model architectures and trained large models end to end (LLMs, VLMs, video or world models, or robot policies), and can explain what went wrong in your runs as well as what worked.

• Deep understanding of modern ML, training dynamics and optimization at scale, and you keep up as the field moves.

• Experience with distributed systems and data pipelines for large-scale training.

• Hands-on multi-node training in PyTorch or JAX, and comfortable building the data and training tooling you need.

• Strong Python and solid engineering; you write research code others can build on.

• You want to work hands-on with robots, not just with models.

Nice to Have:

• Robot learning: imitation learning, VLAs, RL, sim-to-real or dexterous manipulation.

• Models deployed on physical systems under latency and reliability constraints.

• Tactile or force sensing, multi-view video, or world models.

• A PhD, publications or open-source work in robot learning or foundation models.

Why Mind

• Real deployment. Your model is graded on a production line, with a partner committed to scaling it.

• Data no one else has. Data collected directly in the factories where our robots work, alongside third-party data.

• Compute isn't the bottleneck. Large-scale GPU capacity for a small team, so you won't have to fight for GPUs.

• Early ownership. Meaningful equity and a direct say in architecture and research direction.