Senior Applied Research Engineer

CoreWeave•Sunnyvale, CA

1d•$182,000 - $242,000•Onsite

About The Position

The OpenPipe team at CoreWeave is building tools to help agents learn from experience. This is a critical step to make agents reliable enough to perform long tasks autonomously, in the same way human employees are. We're systematically identifying and solving the major bottlenecks between today's tech and those future self-improving agents. The team has released ART (a library for RL), RULER (a reward function), and Serverless RL (an API for RL practitioners). The team is focused on solving challenges related to building simulated environments and improving data efficiency in training methods. This is a hard-tech bet with significant technical risk but low market risk, as there is high demand for self-improving agents. A successful self-improving agent could capture a large fraction of the inference market.

Requirements

Bachelor's, Master's, or PhD degree in Computer Science, Machine Learning, Robotics, or a related technical field.
4+ years of experience in machine learning, or a PhD with 2+ years of relevant industry experience, with a strong focus on model training.
Strong programming skills in Python and hands on experience with modern ML frameworks such as PyTorch or JAX.
Strong understanding of LLM post-training techniques, including supervised fine-tuning, reinforcement learning, and on-policy distillation.
Experience developing, evaluating, and deploying machine learning models in production environments.
Strong research and problem solving skills, with the ability to work effectively in ambiguous, fast moving environments.
Ability to learn fast and ship.
Must be a U.S. person (U.S. citizen or national, U.S. lawful permanent resident, refugee, or asylee) or eligible to access export controlled information without required authorization, or eligible and likely to obtain required authorization.

Nice To Haves

Publications, open source contributions, or demonstrated research impact in LLM post-training or agent learning.
Experience with distributed training, GPU acceleration, and large scale model training systems.
Experience leading technically complex projects or mentoring other engineers.

Responsibilities

Generate and investigate research ideas towards solving the remaining obstacles to continuous learning in production.
Work with the broader OpenPipe team to validate research directions across real customer tasks.
Touch many parts of the stack, from CUDA kernels to high-performance LLM tracing dashboards.
Drive the team's own roadmap and priorities.