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Research Engineer, Reinforcement Learning

tensorstax.com

  • San Francisco, California
  • 30+ days ago

    Highlights

    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. 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.

    Numbers & Facts

    LocationSan Francisco, California

    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.

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