Applied AI Researcher - Robotics & RL, Flexible/Remote

Prime Intellect, Inc.; Oregon; Hybrid; USD 150,000 - 300,000; Full time

Benefits offered by this job

Equity incentives

Flexible Work Locations

Visa sponsorship

Relocation support

Professional development budget

Team off-sites

Job summary

Prime Intellect, Inc. is building the open frontier AI stack, unifying compute, environments, evaluations, sandboxes, and deployment for post-training at frontier scale.

The platform supports SFT, RL, tool use, and continuous production models with open-source models and a full-stack workflow. You will build environments, evaluations, agent systems, and post-training methods with an external robotics team, training agents using RL, world modeling, imitation learning, planning, and multimodal

Qualifications

• Experience in robotics, embodied AI, RL or related fields with practical experiments.
• Hands-on in building/debugging model-training, post-training, and evaluation loops.
• Strong Python; able to produce reusable artifacts for researchers and engineers.
• Comfort with ambiguous problems and updating partners as you progress.
• Evidence of exceptional work through publications, OSS or shipped products.

Responsibilities

• Develop simulation and real-world environments for training and evaluation.
• Create long-horizon agents that operate under hardware constraints and recover from failures.
• Combine offline/online RL, demonstrations, interventions, and feedback for training loops.
• Design rewards and evaluations for safety, adaptation, and continual improvement.

Skills

Python

Experimental design

Reinforcement Learning

Robotics

World models

Computer vision

Agent evaluation

Job description

Prime Intellect, Inc. is building the open frontier AI stack, unifying compute, environments, evaluations, sandboxes, and deployment for post-training at frontier scale.

The platform supports SFT, RL, tool use, and continuous production models with open-source models and a full-stack workflow. You will build environments, evaluations, agent systems, and post-training methods with an external robotics team, training agents using RL, world modeling, imitation learning, planning, and multimodal