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About the Role
Prime Intellect is building the open superintelligence stack, providing infrastructure for AI labs. Our platform unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. We are developing open frontier AI, including open-source models and the platform used to build them. The role involves building post-training and agent systems for national institutions, public-sector organizations, and regulated industries, ensuring compliance with strict boundaries around data, deployment, access, auditability, and human authority.
Responsibilities
- Build improvement loops that run end to end inside the operator's boundary, from trace selection to deployed update.
- Develop agents with explicit permissions, evidence trails, bounded tools, and human checkpoints.
- Create evaluations that test reliability, calibration, recovery, and compliance with real procedures.
- Conduct experiments on how privacy, access, and infrastructure constraints change learning, with methods that transfer across high-assurance settings.
- Turn a workflow into tasks, environments, evaluations, and reward signals.
- Run post-training experiments against real baselines and deploy successful solutions.
- Identify whether gains came from the model or from data, harness, inference, or infrastructure.
- Develop reusable parts into shared research, open-source systems, and product capabilities.
- Work directly with customers, users, or research partners while maintaining a substantive research agenda.
- Build a private improvement loop that turns sensitive traces into environments, training signal, evaluations, and deployable model updates without crossing the operator's data boundary.
- Train a tool-using domain agent whose actions are permissioned, traceable, recoverable, and evaluated against real procedures rather than generic benchmarks.
- Develop high-assurance evaluations for reliability, calibration, robustness, human escalation, and compliance with domain constraints under adversarial or changing conditions.
Requirements
- Relevant experience in RL/post-training, private ML systems, high-assurance evaluation, agent safety, regulated-domain ML, public-sector technical work, or deployment under strict data and infrastructure constraints.
- 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 a willingness to change direction based on 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 current as you resolve them.
- Evidence of exceptional work, such as publications, open-source systems, benchmarks, and shipped products.
- Willingness to build and evaluate these systems directly.
Skills
- Python
Location
- San Francisco
- New York
- London
- Remote
Work Type
- Remote
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.
- Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models.
- We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them.
- The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They 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 — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more.
- We 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.