Research Engineer / Scientist – Reinforcement Learning

Prime Intellect

. Develop and test RL methods for reasoning, coding, and long-horizon tasks, including changes to training objectives and policy optimization .

Posted 10/16/2026full-timeRemote • California • United StatesMid-LevelSenior💰 $150,000 - $350,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in Reinforcement Learning (RL) algorithms and empirical research, with strong programming skills in Python and experience in PyTorch. Proven ability to design experiments, diagnose issues in distributed ML systems, and implement research ideas effectively.

Highest-signal resume keywords

Reinforcement Learning AlgorithmsPython ProgrammingPyTorch FrameworkDistributed Training FrameworksExperimental Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningModel TrainingPolicy OptimizationControlled ExperimentsDebuggingAsynchronous TrainingEmpirical ResearchPerformance EvaluationAlgorithm ImplementationNumerical Stability

Industry Keywords

Distributed ML SystemsTraining ObjectivesTask SelectionLearning DynamicsTraining RecipesPerformance RegressionPolicy StalenessTraining BugsEvaluation ArtifactsLong-Horizon Tasks

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Develop and test RL methods for reasoning, coding, and long-horizon tasks, including changes to training objectives and policy optimization
Investigate stability in asynchronous RL, including policy staleness, trainer/sampler mismatch, and numerical differences between training and rollouts
Work with data and evaluation researchers to understand how task selection, rewards, and training data affect what the model learns
Design experiments to understand how RL performance and learning dynamics change with model size, training compute, and task horizon
Use controlled ablations and evaluations to distinguish learning gains from reward exploitation, evaluation artifacts, or training bugs
Turn experimental findings into training recipes and test whether improvements hold across larger models and longer runs
Extend prime-rl to support new algorithms, models, and asynchronous training workflows
Improve performance and numerical correctness of research runs across trainers, rollout workers, and environments with the infrastructure team
Build diagnostics that make unstable runs and performance regressions easier to reproduce and help distinguish algorithm problems from implementation or runtime issues

Requirements

What you’ll need

Demonstrated depth in at least one of RL algorithms, empirical research on model training, or distributed ML systems
Strong programming skills in Python and experience with PyTorch or a comparable ML framework
Ability to design controlled experiments, interpret results, and debug unexpected model or system behavior
Experience taking a research idea or systems change through implementation, testing, and evaluation, with a clear account of what worked and what didn’t
Experience with distributed training frameworks or asynchronous rollout pipelines
Experience diagnosing unstable RL runs, including policy-staleness or numerical issues
Work on coding agents, verifiable rewards, or long-horizon environments

Benefits

Comp & perks

Equity incentives
Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco
Visa sponsorship and relocation assistance for international candidates
Quarterly team off-sites
Hackathons
Conferences and learning opportunities
Opportunity to work with a talented, hard-working and mission-driven team