Applied Researcher - Scientific AI

The role

You will build and post-train agents for scientific research. This includes building environments for literature search, code execution, and experiment selection.

What you will do

• Build long-horizon environments for agents to search literature, query databases, run code, and choose experiments.
• Turn research partners' workflows, datasets, and outcome criteria into trainable tasks, evaluations, and reward signals.
• Design verifiers for correctness, novelty, and reproducibility.
• Connect programmatic checks, simulations, domain models, and expert feedback to training.
• Develop RL and multi-agent methods for proposing, testing, and critiquing hypotheses.
• Keep code, data, logs, and experiment configurations reproducible.
• Release reusable environments, evaluations, and research code.
• Work directly with customers, users, or research partners.

Required qualifications

• Depth in RL, agents, or post-training systems.
• Ability to acquire the relevant scientific context.
• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.
• Strong experimental judgment.
• Strong Python.
• Ability to build artifacts that other researchers and engineers can run, inspect, and extend.
• Comfort owning ambiguous problems.

Nice to have

• Prior work in autonomous research, ML for science, scientific computing, or reproducible computational research is useful

Benefits

• Cash Compensation Range of $150-300k + equity incentives
• Flexible Work (San Francisco, New York, London, Berlin, or remote)
• Visa Sponsorship & relocation support
• Professional Development budget
• Team Off-sites & conference attendance

About the company

Prime Intellect is building the open superintelligence stack. The platform Lab unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system.