Senior Applied Research Engineer

CoreWeave•Sunnyvale, WA

21h•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. We have released ART, the easiest library for getting started with RL. We developed RULER, a general-purpose reward function that works across many diverse tasks. We built Serverless RL, an elegant API that gives RL practitioners full control over their data, environment and reward function while letting them outsource the headaches of managing GPU infrastructure. These releases have a theme: we're systematically tackling each major roadblock to successfully training self-improving agents. Several serious challenges remain. Building simulated environments often requires substantial human labor, and existing training methods are not data efficient enough. We're laser-focused on solving these problems and making self-improvement a reality for agent developers. In startup terms, this is a classic hard-tech bet. Our roadmap involves substantial technical risk; there are still major technical problems we're facing without a proven solution. However, there is very little market risk. We've worked closely with the teams building agents at many of the top AI-native startups as well as large enterprises. If we can build this, everyone will want it. A self improving agent that learns from experience the way a human employee would could quickly capture a large fraction of the total inference market, which is worth tens of billions of dollars today and will be worth hundreds of billions in a few years.

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 export 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 these research directions across real customer tasks.
Touch many parts of the stack, from CUDA kernels to high-performance LLM tracing dashboards.
Drive the OpenPipe team's roadmap and priorities.