Research Engineer, Reinforcement Learning at TensorStax — San Francisco, USA | Base Career

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

Research Engineer, Reinforcement Learning

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