Research Engineer, Reinforcement Learning

Responsibilities

• Develop and refine reward functions to optimize agent behavior for complex data engineering tasks.
• Create RL gym environments for language model agents.
• Fine-tune language models using reinforcement learning techniques such as PPO, DPO, and KTO.
• Stay at the forefront of research on RL for language models, incorporating advancements like GRPO, SWE-Gym, and SWE-RL into practical applications.
• Curate and build high-quality datasets for supervised fine-tuning (SFT) and RLHF.
• Design experiments to evaluate and improve the agentic capabilities of language models in data environments.

Requirements

• Deep understanding of reinforcement learning, reward shaping, and optimization strategies.
• Strong familiarity with LLM fine-tuning techniques (PPO, DPO, KTO) and their applications in reinforcement learning.
• Knowledge of recent advancements in RL for language models (GRPO, SWE-Gym, SWE-RL).
• Experience curating and constructing high-quality datasets for fine-tuning.
• Strong problem-solving skills and a history of working on complex ML projects.
• High agency—ability to work independently, experiment proactively, and drive research initiatives forward.

Nice-to-Haves

• Experience with distributed training in PyTorch (DDP, FSDP).
• Hands-on experience designing RL environments for traditional RL problems.
• Contributions to open-source projects in RL, LLMs, or ML infrastructure.
• Familiarity with data lakes and warehouses (Snowflake, BigQuery, Redshift).

Benefits

• 100% employer-covered health, dental, and vision insurance.
• 401(k) with company match.
• Access to Bay Club or Equinox in San Francisco.