Research Engineer, Reinforcement Learning
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Before you apply
LocationSan Francisco, CA, San Francisco, United States
Work modehybrid
Typefull-time
DepartmentEngineering
Company size4+ people
PostedMar 17, 2025 · 1y ago
Verified live2d ago
May no longer be actively hiring
Posted by the company on Mar 17, 2025
At a glanceSummarised by Seekless from the posting.
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
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)
Skills
Python
PyTorch
PPO
DPO
KTO
GRPO
SWE-Gym
SWE-RL
Snowflake
BigQuery
Redshift
DDP
FSDP
Benefits
Health insurance
Dental insurance
Vision insurance
401(k)
Gym membership
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.
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Fine-tune language models using reinforcement learning techniques such as PPO, DPO, and KTO.
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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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