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# Research Engineer - Reinforcement Learning

- **Company:** Tensorstax, Inc.
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, BigQuery, Information Engineering, Data Files, Data Infrastructure, Data Warehousing, Distributed Computing Environment, Language Modeling, Open Source Technology, Reinforcement Learning, Pytorch, Large Language Models, Snowflake, Data Lakes, Star Schema, Data Management, Machine Learning Operations, Amazon Redshift
- **Published:** September 20, 2026
- **Apply:** https://www.careerbuilder.com/job-details/research-engineer-reinforcement-learning-san-francisco-ca--d5fc1dfc-0df6-450c-b024-feb758b0729b

## About the Role

* 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)., Artificial Intelligence (AI), Data Management, Data Modeling Language, Data Sets, Data Warehousing, Experiment Design, Identify Issues, Modeling Languages, Open Source, Preferred Provider Organization (PPO), Problem Solving Skills, Reinforcement Learning, Snowflake Schema, Systems Administration/Management

## Description

TensorStax 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.


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