Applied Research – Scientific AI

Prime Intellect

. Build and post-train agents for scientific research, including literature search, code execution, domain-tool use, and experiment selection .

Posted 10/19/2026full-timeRemote • California • United States, United KingdomMid-LevelSenior💰 $150,000 - $300,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Reinforcement Learning (RL), agent systems, and post-training methodologies, with a strong focus on building reproducible computational research environments and experimental evaluations. Proven ability to collaborate with research partners and translate complex workflows into trainable tasks while maintaining a substantive research agenda.

Highest-signal resume keywords

Reinforcement Learning (RL)Python ProgrammingModel-Training SystemsExperimental JudgmentAutonomous Research

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningAgent SystemsPost-Training SystemsModel-TrainingExperimental EvaluationScientific Database QueriesCode ExecutionDomain-Tool UseData AnalysisHypothesis Testing

Soft Skills

Strong Experimental JudgmentProblem OwnershipCollaborationAdaptabilityCommunication

Tools & Technologies

Scientific Computing ToolsResearch DatabasesSimulation SoftwareEvaluation FrameworksOpen-Source Systems

Industry Keywords

Computational ScienceReproducible ResearchMachine Learning for ScienceResearch PartnershipsExperimental Feedback

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Build and post-train agents for scientific research, including literature search, code execution, domain-tool use, and experiment selection
Work across autonomous research and computational science with environments and evaluations built around research partners' tools, data, and experimental feedback
Work directly with customers, users, or research partners while maintaining a substantive research agenda
Build long-horizon environments for literature search, scientific database queries, code and domain-tool execution, and experiment selection within fixed budgets
Convert research partners' workflows, datasets, and outcome criteria into trainable tasks, evaluations, and reward signals
Design verifiers for correctness, novelty, and reproducibility, including checks for label leakage, lookup shortcuts, and unsupported conclusions
Connect programmatic checks, simulations, domain models, and expert feedback to training; calibrate confidence estimates and define human-review requirements
Develop RL and multi-agent methods for proposing, testing, and critiquing hypotheses
Compare methods with strong single-agent baselines at equal compute and evaluate on held-out problems
Maintain reproducible code, data, logs, and experiment configurations; rerun results, measure noise, perform ablations, and release reusable environments, evaluations, and research code

Requirements

What you’ll need

Depth in RL, agents, or post-training systems and ability to acquire relevant scientific context
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 willingness to change direction when evidence changes
Strong Python
Ability to build artifacts that other researchers and engineers can run, inspect, and extend
Comfort owning ambiguous problems and keeping partners and teammates current
Evidence of exceptional work through publications, open-source systems, benchmarks, or shipped products

Benefits

Comp & perks

Equity incentives
Flexible Work (San Francisco, New York, London, or remote)
Visa Sponsorship & relocation support
Professional Development budget
Team Off-sites & conference attendance