Staff Applied Research Engineer, CoreWeave

• You have trained LLMs to be SOTA on specific tasks
• You have opinions on whether sequence-level or token-level importance ratios are more effective
• You probably shared the ScaleRL paper in your group chats, and kicked off a few ablations after you read it
• 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
• Beyond your role's specific qualifications, we're looking for strong engineers with great taste
• The most important qualification by far is that you learn fast and can ship
• We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match
• This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency

Desirable

• 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
• 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
• 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
• This is an applied research role
• You will be expected to generate and investigate research ideas towards solving the remaining obstacles to continuous learning in production
• You will work with the broader OpenPipe team to validate these research directions across real customer tasks
• We are very GPU rich and are ready to direct an enormous amount of compute at this effort
• This role will inevitably involve a lot of learning on the job; we're building this airplane as we fly it
• Engineers on our team touch everything from CUDA kernels to high-performance LLM tracing dashboards, and you will have an opportunity to touch many parts of this stack
• Although we operate as part of a larger company, the OpenPipe team is small, has a large degree of autonomy and drives our own roadmap and priorities
• This is an excellent role for someone looking to found their own company in the future