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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 the platform used to build them. The role focuses on reinforcement learning for agents that reason over long horizons, use tools, coordinate, recover from failure, and improve from experience, working on RL and ML systems across various application domains.
Responsibilities
- Turn workflows into tasks, environments, evaluations, and reward signals.
- Run post-training experiments against real baselines and deploy successful methods.
- Determine if gains originate from the model, data, harness, inference, or infrastructure.
- Develop reusable research components, open-source systems, and product capabilities.
- Work directly with customers, users, or research partners while maintaining a research agenda.
- Extend existing work on verifiers, algorithms, multi-agent training, and RL systems.
- Pressure-test current work against live application programs.
- Build agentic judges and credit-assignment methods for trajectory inspection and learning signals.
- Ensure reliable training of multi-agent and long-horizon environments at scale.
- Detect and address issues like reward hacking, harness behavior, data leakage, or infrastructure artifacts.
- Own RL and post-training methods for tool-using, multi-agent, memory-augmented, and long-horizon systems.
- Develop reusable environments, harnesses, rewards, and evaluations.
- Conduct experiments connecting algorithm choices to training and inference behavior.
- Create open-source systems and papers based on insights from application researchers and external users.
Requirements
- Hands-on RL training, scalable ML systems, agent environments, evaluation, inference/training infrastructure, or research crossing methods and systems.
- 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 extensible artifacts for researchers and engineers.
- Comfort owning ambiguous problems and maintaining clear communication with partners and teammates.
- Evidence of exceptional work through publications, open-source systems, benchmarks, or shipped products.
- Demonstrate something difficult built, including failures and how the approach evolved based on evidence.
Skills
- Reinforcement Learning (RL)
- Machine Learning (ML) systems
- Agent-based systems
- Long-horizon reasoning
- Tool use
- Multi-agent coordination
- Scalable ML systems
- Experimental design
- Python
Location
- San Francisco
- New York
- London
- Remote
Work Type
- Remote
- Full-time
Experience Level
- Applied Research
Salary/Compensations
- $150-300k
Benefits
- 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.
- 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.