Research Engineer, Reinforcement Learning

Tensorstax Com

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San Francisco, CA on site Until 8/21/2026 First posted May 25, 2026 Last posted May 25, 2026
Job description
Research Engineer – Reinforcement Learning
Location: San Francisco (Hybrid)

About TensorStax
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.

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.
About this role

Summary

Develop reinforcement learning techniques and environments for language models in data infrastructure tasks.

Job title

Research Engineer, Reinforcement Learning

Experience level

Industry

software

Location requirements

San Francisco, hybrid work allowed

Salary

Not specified

Management role

No

Skills & keywords

Required skills

reinforcement learningreward shapingoptimizationPPODPOKTOdataset curationPyTorchdistributed training

Preferred skills

RL environmentsopen-source contributionsdata lakesRedshift

Specializations

reinforcement learninglanguage modelsdataset curationRL fine-tuning
Locations

Structured locations inferred from the posting.

San Francisco, CA, USA

Hybrid City