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About the Role
Prime Intellect is building the open superintelligence stack, providing infrastructure for AI labs. 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 has raised $150M and seeks individuals passionate about building at the intersection of frontier research, real infrastructure, and go-to-market strategies for a nascent category.
Responsibilities
- Build and post-train agents for scientific research, including literature searching, code execution, domain tool utilization, and experiment selection.
- Develop experimental loops for scientific tools and datasets, similar to autonomous-research studies involving multi-day optimization tasks.
- Construct long-horizon environments for agents to search literature, query scientific databases, run code and domain tools, and select experiments within budget constraints.
- Translate research partners' workflows, datasets, and outcome criteria into trainable tasks, evaluations, and reward signals.
- Design verifiers for correctness, novelty, and reproducibility, ensuring agents use underlying evidence and checking for issues like label leakage or unsupported conclusions.
- Integrate programmatic checks, simulations, domain models, and expert feedback into training, accounting for noisy results and defining needs for human review.
- Develop and compare RL and multi-agent methods for hypothesis generation, testing, and critique against single-agent baselines.
- Ensure code, data, logs, and experiment configurations are reproducible, re-running results, measuring noise, and using ablations to isolate model improvements.
- Release reusable environments, evaluations, and research code.
Requirements
- Demonstrated depth in RL, agents, or post-training systems, with the ability to acquire relevant scientific context.
- Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely related experimental loops.
- Strong experimental judgment, including the use of meaningful baselines, careful trace inspection, clear failure attribution, and adaptability to changing evidence.
- Proficiency in Python and the ability to create artifacts that are runnable, inspectable, and extensible by other researchers and engineers.
- Comfort with owning ambiguous problems and maintaining clear communication with partners and teammates.
- Evidence of exceptional work through publications, open-source systems, benchmarks, or shipped products, including a description of a difficult project, its failures, and how evidence influenced the approach.
Skills
- RL
- Agents
- Post-training systems
- Python
Location
- San Francisco
- New York
- London
- Remote
Work Type
- Remote
Experience Level
- Prior work in autonomous research, ML for science, scientific computing, or reproducible computational research is useful but not required.
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.