Applied Robotics Researcher - Frontier AI, Remote

Prime Intellect, Inc.; San Francisco (CA); Hybrid; USD 150,000 - 300,000; Full time

Benefits offered by this job

Flexible work locations

Visa sponsorship

Development budget

Team off-sites

Job summary

Prime Intellect, Inc. is building open frontier AI infrastructure and research platforms in San Francisco. We seek researchers who will design environments, evaluators, and agent systems, training agents with RL, world modeling, and planning against long-horizon tasks with real robots.

You will collaborate with external robotics teams to run post-training experiments, evaluate outcomes, and contribute to reusable research, open-source systems, and production capabilities.

Qualifications

• Relevant experience in 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 with clear failure attribution and willingness to pivot when evidence changes.
• Strong Python skills and ability to build artifacts that others can run, inspect, and extend.

Responsibilities

• Own simulation and real-world environments for training, evaluation, and careful sim-to-real comparison.
• Develop long-horizon agents that perceive, plan, act, remember, and recover under hardware constraints.
• Create training pipelines combining offline/online RL, demonstrations, interventions, and outcome feedback.
• Design rewards and evaluations for task success, safety, adaptation, and continual improvement.

Skills

Robotics

Embodied AI

Reinforcement learning

World models

Simulation

Imitation learning

Planning

Multimodal perception

Python

Experiment design

Job description

Prime Intellect, Inc. is building open frontier AI infrastructure and research platforms in San Francisco. We seek researchers who will design environments, evaluators, and agent systems, training agents with RL, world modeling, and planning against long-horizon tasks with real robots.

You will collaborate with external robotics teams to run post-training experiments, evaluate outcomes, and contribute to reusable research, open-source systems, and production capabilities.