Applied Research Engineer

What You’ll Do

The Open Pipe team at Core Weave 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. So far, we’ve released ART, the easiest library for getting started with RL;

developed RULER, a general‑purpose reward function that works across many diverse tasks; and built Serverless RL, an elegant API that gives RL practitioners full control over their data, environment and reward function while outsourcing 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, such as the need for substantial human labor to build simulated environments and the lack of data‑efficient training methods.

We’re laser‑focused on solving these problems and making self‑improvement a reality for agent developers.

This is an applied research role. You will generate and investigate research ideas toward solving the remaining obstacles to continuous learning in production, and work with the Open Pipe team to validate these directions across real customer tasks. We’re GPU‑rich and ready to direct an enormous amount of compute at this effort. Beyond the specific qualifications, we’re looking for strong engineers who learn fast and can ship.

You’ll touch everything from CUDA kernels to high‑performance LLM tracing dashboards, and you’ll have the opportunity to influence the entire stack. The Open Pipe team is small, has a large degree of autonomy, and drives our own roadmap and priorities. This role is an excellent opportunity for someone looking to build their own company in the future.

• 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 judgement, 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.

• 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.