Autonomous Scientific AI Researcher — Remote

Prime Intellect AI; San Francisco (CA); Remote; USD 150,000 - 300,000; Full time

Benefits offered by this job

Cash compensation

Flexible work locations

Visa sponsorship

Development budget

Team off-sites

Job summary

Prime Intellect AI is building the open frontier AI platform and seeking researchers to build post-training agents for scientific research. You will enable autonomous literature search, code execution, tool use, and guided experimentation within defined compute budgets.

The role involves collaboration with research partners, designing robust evaluation loops, and ensuring reproducibility across experiments. Strong Python and artifact-building skills are essential.

Qualifications

• Depth in RL, agents, or post-training systems.
• Prior work in autonomous research, ML for science, scientific computing, or reproducible computational research is useful but not required.
• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.
• Strong experimental judgment: meaningful baselines, careful trace inspection, clear failure attribution, and a willingness to change direction when the evidence changes.
• Strong Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.

Responsibilities

• Build long-horizon environments where agents search literature, query scientific databases, run code and domain tools, and choose experiments within a fixed compute or experimental budget.
• Turn research partners' workflows, datasets, and outcome criteria into trainable tasks, evaluations, and reward signals.
• Design verifiers for correctness, novelty, and reproducibility. Test whether agents use the underlying evidence, with checks for label leakage, lookup shortcuts, and unsupported conclusions.
• Connect programmatic checks, simulations, domain models, and expert feedback to training. Account for noisy or delayed results, calibrate confidence estimates, and define where an experiment needs human review.
• Develop RL and multi-agent methods for proposing, testing, and critiquing hypotheses. Compare them with strong single-agent baselines at equal compute and evaluate on held-out problems.
• Keep code, data, logs, and experiment configurations reproducible. Re-run promising results, measure noise, and use ablations to separate model improvements from changes to the data, harness, or inference setup. Release reusable environments, evaluations, and research code.

Skills

RL depth

Agents

Post-training systems

Python

Tools

ML frameworks

Experiment tooling

Job description

Prime Intellect AI is building the open frontier AI platform and seeking researchers to build post-training agents for scientific research. You will enable autonomous literature search, code execution, tool use, and guided experimentation within defined compute budgets.

The role involves collaboration with research partners, designing robust evaluation loops, and ensuring reproducibility across experiments. Strong Python and artifact-building skills are essential.