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About the Role
Prime Intellect is building the open superintelligence stack, providing infrastructure for ambitious AI teams. Their platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. They are developing open frontier AI, including open-source models and a full-stack platform for AI research and development. The company seeks individuals passionate about building at the intersection of frontier research, real infrastructure, and go-to-market strategy in a nascent category.
Responsibilities
- Build environments, evaluations, agent systems, and post-training methods with an external robotics team.
- Train and evaluate agents using reinforcement learning, world modeling, imitation learning, planning, and multimodal perception.
- Utilize feedback from simulation and deployed robots for agent training and evaluation.
- Test agent behavior under partial observations, long horizons, changing environments, and hardware/safety constraints.
- Turn workflows into tasks, environments, evaluations, and reward signals.
- Run post-training experiments against real baselines and deploy successful solutions.
- Determine if gains come from the model, data, harness, inference, or infrastructure.
- Develop reusable components into shared research, open-source systems, and product capabilities.
- Work directly with customers, users, or research partners while maintaining a research agenda.
- Build aligned simulation and real-world evaluations for long-horizon embodied tasks and measure policy transfer failures.
- Combine demonstrations, interventions, offline data, and online feedback into a training loop for improved behavior without prior competence loss.
- Train embodied agents to recover from partial failures, changing scenes, and uncertain states while respecting safety and hardware constraints.
- Own simulation and real-world environments for training, evaluation, and sim-to-real comparison.
- Develop long-horizon agents capable of perception, planning, acting, remembering, and recovery under hardware constraints.
- Implement training that combines offline and online RL, demonstrations, interventions, and outcome feedback.
- Create rewards and evaluations for task success, safety, adaptation, and continual improvement without silent regressions.
Requirements
- 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, including meaningful baselines, careful trace inspection, clear failure attribution, and adaptability to evidence.
- Strong Python skills and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.
- Comfort owning ambiguous problems and keeping partners and teammates informed of progress.
- Evidence of exceptional work, such as publications, open-source systems, benchmarks, or shipped products.
- Ability to demonstrate something difficult that was built, including what failed and how the evidence changed the approach.
Skills
- Robotics
- Embodied AI
- Reinforcement Learning (RL)
- Imitation Learning
- Control
- World Models
- Simulation
- Computer Vision
- Agent Evaluation
- Production Systems
- Python
Location
- San Francisco
- New York
- London
- Remote
Work Type
- Remote
- Onsite
Experience Level
- Mid-level
- Senior
Salary/Compensations
- $150-300k
Benefits
- Cash Compensation Range of $150-300k + equity incentives
- Flexible Work
- Visa Sponsorship & relocation support
- Professional Development budget
- Team Off-sites & conference attendance
About the Company
- Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.
- Their platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale.
- They are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform their own research team uses to build them.
- The next generation of AI companies, enterprises, and research teams need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.
- Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators.
- They are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.