Applied Scientist: Robotic Learning & Dexterity

Generalist; San Mateo, Somerville (CA, MA); Hybrid; USD 150,000 - 200,000; Full time

Job summary

Generalist is building embodied foundation models focused on dexterity and real-world robotics. As an Applied Scientist you will define capabilities, design evaluations, run experiments across model variants, and drive post-training iterations that translate lab results into robust, reusable skills.

This hybrid role blends research, engineering and operations, requiring rigor in experiments, data analysis at scale, and the ability to communicate findings to research and partner teams.

Qualifications

• PhD in a relevant area preferred.
• Strong background in machine learning and robot learning.
• Experience designing evaluations and conducting large-scale experiments.
• Ability to communicate findings to research and partner teams.

Responsibilities

• Own end-to-end improvement of target capabilities, from baseline measurement to demonstrated mastery.
• Design task suites and evaluation protocols that generalize beyond a single deployment.
• Coordinate data collection, training and fine-tuning, and orchestrate large-scale evaluations.
• Run rigorous experimental analysis across model variants, isolate variables and diagnose failure modes.

Skills

Machine learning

Robot learning

Experimentation

Data analysis at scale

Communication of findings

Education

PhD in a relevant area

Tools

Python

PyTorch

Experimentation frameworks

Job description

Generalist is building embodied foundation models focused on dexterity and real-world robotics. As an Applied Scientist you will define capabilities, design evaluations, run experiments across model variants, and drive post-training iterations that translate lab results into robust, reusable skills.

This hybrid role blends research, engineering and operations, requiring rigor in experiments, data analysis at scale, and the ability to communicate findings to research and partner teams.