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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 quantitative-research environments where agents form hypotheses, run analyses, backtest on point-in-time data, diagnose results, and determine next steps.
- Design rewards and evaluations to differentiate durable improvement from artifacts like look-ahead bias, data leakage, overfitting, favorable execution assumptions, or hidden concentration and tail risk.
- Train long-horizon agents to utilize data, research, coding, simulation, and portfolio tools while maintaining provenance, respecting risk constraints, and adapting to changing market conditions.
- Develop environments grounded in point-in-time data, realistic market mechanics, transaction costs, latency, capacity, and operational constraints.
- Create agents capable of conducting quantitative research, using analytical and execution tools, inspecting evidence, and recovering from failed hypotheses.
- Build verifiers and evaluations to test robust out-of-sample behavior across various assets, horizons, regimes, and adverse conditions.
- Implement production loops with monitoring, human authority, regression gates, and failure attribution across models, data, simulation, and infrastructure, producing methods transferable to other high-stakes decision systems.
Requirements
- Demonstrated depth in RL, agents, or post-training systems with the ability to become fluent in quantitative research.
- Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.
- Strong experimental judgment, including the use of meaningful baselines, careful trace inspection, clear failure attribution, and adaptability based on evidence.
- Proficiency in Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.
- Comfort in owning ambiguous problems and maintaining clear communication with partners and teammates during resolution.
- Evidence of exceptional work through publications, open-source systems, benchmarks, or shipped products, including a demonstration of a difficult project, its failures, and how the approach evolved based on evidence.
Skills
- Reinforcement Learning (RL)
- Agent-based systems
- Post-training systems
- Quantitative research
- Python
- Systematic trading
- Market microstructure
- Time-series modeling
- Econometrics
- Financial simulation
Location
- San Francisco
- New York
- London
- Remote
Work Type
- Full-time
Experience Level
- Not specified
Education Level
- Not specified
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, providing the infrastructure frontier AI labs build internally 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 numerous AI, infrastructure, and enterprise operators.
- The company is looking for individuals 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.
Equal Opportunity
- Not specified