Research Engineer, Reinforcement Learning

TensorStax.com

Office

San Francisco, CA

Full Time

Research Engineer – Reinforcement LearningLocation: San Francisco (Hybrid)
About TensorStaxTensorStax is building fully autonomous AI systems to manage and maintain mission-critical data infrastructure and pipelines. We leverage reinforcement learning to enhance language models' ability to reason over large-scale data lakes and warehouses, detect pipeline failures, construct new pipelines with high precision, and enable agentic behavior—allowing systems to proactively identify and resolve issues autonomously.
As a Research Engineer specializing in Reinforcement Learning, you will:

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.

What We’re Looking For:

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.

Bonus Points:

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.