Applied Researcher - Robotics

The role

You will build environments, evaluations, agent systems, and post-training methods with an external robotics team. You will train and evaluate agents using reinforcement learning, world modeling, imitation learning, planning, and multimodal perception.

What you will do

• Build aligned simulation and real-world evaluations for long-horizon embodied tasks
• Combine demonstrations, interventions, offline data, and online feedback into a training loop
• Train an embodied agent to recover from partial failures, changing scenes, and uncertain state
• Own simulation and real-world environments for training and evaluation
• Develop long-horizon agents that perceive, plan, act, remember, and recover
• Create training that combines offline and online RL, demonstrations, interventions, and outcome feedback
• Develop rewards and evaluations for task success, safety, adaptation, and continual improvement
• Work directly with customers, users, or research partners

Required qualifications

• Relevant experience: robotics, embodied AI, RL, imitation learning, control, world models, simulation, computer vision, agent evaluation, or production systems interacting with the physical world
• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops
• Strong experimental judgment
• Strong Python
• Ability to build artifacts that other researchers and engineers can run, inspect, and extend
• Comfort owning ambiguous problems

Nice to have

• Publications
• Open-source systems
• Benchmarks
• Shipped products

Benefits

• Cash Compensation Range of $150-300k + equity incentives
• Flexible Work (San Francisco, New York, London, or remote)
• Visa Sponsorship & relocation support
• Professional Development budget
• Team Off-sites & conference attendance

About the company

Prime Intellect is building the open superintelligence stack. The platform Lab unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system.