Research Engineer, Post Training RL

TensorStax; San Francisco (CA); On-site; USD 100,000 - 300,000; Full time

Job summary

A leading AI software company is seeking a Research Engineer specializing in Reinforcement Learning to optimize data engineering tasks with cutting-edge AI techniques. The role involves developing reward functions, fine-tuning models, and conducting research to advance the capabilities of autonomous systems. Ideal candidates will possess a strong understanding of reinforcement learning and the ability to work independently. With competitive compensation and comprehensive benefits like full health coverage and 401(k) match, this position offers a dynamic opportunity in a growing field.

Benefits

100% employer-covered health, dental, and vision insurance

401(k) with company match

Access to Bay Club or Equinox

Qualifications

• Deep understanding of reinforcement learning and optimization strategies.
• Experience with LLM fine-tuning techniques (PPO, DPO, KTO).
• Strong problem-solving skills and experience with complex ML projects.

Responsibilities

• Develop and refine reward functions for optimizing agent behavior.
• Fine-tune language models using reinforcement learning techniques.
• Curate and build datasets for supervised fine-tuning and RLHF.

Skills

Reinforcement Learning

Problem Solving

Data Engineering

Tools

PyTorch

Data Lakes

Data Warehouses

Job description

Direct message the job poster from TensorStax

co-founder @ tensorstax - making data engineering easier with AI

Research Engineer – Post Training Reinforcement Learning

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.

What You’ll Do

As a Research Engineer specializing in Reinforcement Learning, you will:

• Develop and refine reward functions to optimize agent behavior for complex data engineering tasks.
• 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).
• 100% employer-covered health, dental, and vision insurance.
• 401(k) with company match.
• Access to Bay Club or Equinox in San Francisco.

Seniority level

• Seniority level

  Entry level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Software Development

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