About TensorStax

TensorStax is building the next generation of autonomous agents for data engineering. Backed by a $5M seed round, we're developing an LLM Compiler, agent framework, and reinforcement learning infrastructure purpose-built for structured data workflows. Our vision is to unify the modern data stack behind intelligent agents that can plan, debug, and optimize complex pipelines across dbt, Airflow, Spark, and beyond.

The Role

We're looking for a Research Engineer Intern to help our team build simulation environments that mirror real-world data engineering workflows. These environments are core to our RL training stack - they allow us to safely and scalably train and evaluate agents across realistic DAGs, job failures, and data state transitions.

In this role, you'll work with our research and systems teams to:

• Build and maintain simulated environments based on real data stack components (e.g., Airflow DAGs, dbt projects, Spark jobs)
• Script, parameterize, and modularize workloads across various data tools
• Set up realistic failure modes, delays, and edge cases for agents to learn from
• Help us wrap environments with consistent interfaces used in RL training
• Collaborate with ML researchers to ensure environments are reproducible and scalable

About You

• Hands-on experience with data engineering tools: Spark (PySpark or Scala), Airflow, and dbt
• Proficient in Python, with clean code and testing practices
• Strong systems mindset - comfortable working across infra, config, and orchestration layers
• Familiarity with containerization tools (Docker, etc.) and cloud environments is a plus
• Interest in reinforcement learning or agent systems is a plus (but not required)

Bonus: experience with dataset generation, simulation environments, or pipeline testing

Why Join

• Work directly on infrastructure that trains intelligent agents in realistic, high-leverage environments
• Learn how cutting-edge LLM and RL research gets translated into production systems
• Collaborate with a world-class research team focused on real technical depth, not just demos
• Flexible work setup, fast pace, strong mentorship, and a chance to own meaningful projects