Advance reinforcement learning for large language models, developing, implementing, and running training methods to help models solve frontier-level challenging tasks.
Locations (2)
New York City, New York, United States
San Francisco, California, United States
Compensation
$200,000—$375,000 USD
Responsibilities
Research and implement cutting-edge reinforcement learning algorithms and training recipes for large language models, adapting and extending methods such as GRPO for complex, multi-turn, and long-horizon tasks
Design and build data pipelines that generate high-quality training signals for RL workflows, including AI-assisted data annotation and curation data pipelines to improve model generalization to unseen benchmarks
Prototype and iterate on end-to-end RL training recipes, using training and evaluation results to identify what improves performance and where tasks, environments, or training signals need to change
Research Scientist Intern, Robotics Foundation Models, Agentic RL (PhD)
This internship focuses on rigorous benchmarking of machine learning and optimization methods for inverse design and optimization. You will develop a reproducible benchmarking framework for comparing an existing engineering design workflow with selected academic and open-source methods across representative inverse design and optimization problems.
Responsibilities
Review relevant state-of-the-art research and available open-source implementations in inverse design, black-box optimization, reinforcement learning, and related methods as appropriate to the selected use cases.
Select a small set of technically meaningful baseline methods together with the supervising researchers.
Define representative engineering optimization problems with explicit design variables, objectives, constraints, parameter ranges, and simulation settings.
Establish matched experimental protocols covering initialization, random seeds, evaluation budgets, stopping criteria, and training or model-building costs.
Adapt and integrate baseline implementations with existing engineering simulation workflows.
Requirements
PhD currently pursuing or recently completed in Machine Learning, Computer Science, Electrical or Electronic Engineering, Applied Mathematics, or a related technical field
Work with research scientists, ML engineers, data teams, and delivery teams to diagnose training results, turn findings into actionable improvements to data and environments, and validate those improvements in subsequent training runs
Stay current with advances in large-scale distributed LLM training and scalable RL, and apply relevant methods to our training systems and challenging environments, including tool-use and other complex tasks
Requirements
Ph.D. in machine learning, reinforcement learning, or a related field
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