Staff Applied Research Engineer

CoreWeave•Sunnyvale, WA

20h•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

8+ years of experience in machine learning or applied research, or a PhD with 4+ years of relevant industry experience.
Demonstrated success developing LLM training methods or systems that produce meaningful improvements on real-world tasks.
Deep expertise in LLM post-training, including supervised fine-tuning, reinforcement learning, on-policy distillation, reward modeling, and policy optimization.
Strong research judgment, including the ability to identify high-impact problems, design rigorous experiments, and make decisions from ambiguous results.
Experience taking research ideas from initial hypothesis through implementation, evaluation, and production deployment.
Proven ability to set technical direction, lead complex cross functional initiatives, and mentor other engineers.
Learn fast and can ship.
Access to export controlled information requires being a U.S. person (U.S. citizen or national, U.S. lawful permanent resident, refugee, or asylee) or being eligible to access the information without required export authorization, or eligible and likely to obtain the required export authorization.

Nice To Haves

Publications, open source contributions, or other demonstrated research impact in reinforcement learning, LLM post-training, or agent learning.
Deep experience with distributed training, GPU optimization, and large-scale model training systems.

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 our own roadmap and priorities as part of the small, autonomous OpenPipe team.