Staff Applied Research Engineer

Weights & Biases Weights & Biases · Data AI · Bellevue, WA +1 · Technology

CoreWeave's OpenPipe team is building tools for agents to learn from experience, aiming to make them reliable for autonomous long tasks. The role involves applied research to solve obstacles in continuous learning for production, focusing on LLM post-training, reinforcement learning, and agent development. The team has released tools like ART, RULER, and Serverless RL, and is seeking someone with deep expertise in LLM training and a strong research background to contribute to self-improving agents.

What you'd actually do

  1. generate and investigate research ideas towards solving the remaining obstacles to continuous learning in production
  2. work with the broader OpenPipe team to validate these research directions across real customer tasks
  3. take research ideas from initial hypothesis through implementation, evaluation, and production deployment
  4. set technical direction, lead complex cross functional initiatives, and mentor other engineers

Skills

Required

  • 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

Nice to have

  • 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

What the JD emphasized

  • technical risk
  • market risk
  • learn fast
  • ship
  • continuous learning in production

Other signals

  • continuous learning in production
  • self-improving agents
  • LLM training methods
  • reinforcement learning