Senior Applied ML Engineer
$182k - $242k • Remote • Bellevue, WA / Sunnyvale, CA
Posted 5d ago
Job Location
Bellevue, WA / Sunnyvale, CA
Tech Stack
Remote Work Policy
Fully remote
Categories
Applied AI Engineer
About the job
The CRAFT (Capabilities, Routing, Adaptation & Fine-Tuning) team at CoreWeave is focused on developing tools that enable AI agents to learn from experience, a crucial step towards autonomous long-task execution. The team has already released innovative projects like ART for RL, RULER for reward functions, and Serverless RL for infrastructure management. They are now tackling remaining challenges in agent training, such as reducing human labor in environment creation and improving data efficiency. This role offers a significant opportunity to contribute to hard-tech advancements in a rapidly growing market, with the potential to shape the future of self-improving AI agents.
Responsibilities
- Generate and investigate research ideas to solve obstacles in continuous learning for production AI agents.
- Collaborate with the CRAFT team to validate research directions using real customer tasks.
- Contribute to various aspects of the technology stack, from CUDA kernels to LLM tracing dashboards.
- Learn quickly and ship impactful solutions in a fast-paced, ambiguous environment.
- Develop, evaluate, and deploy machine learning models in production.
Requirements
- Trained LLMs to state-of-the-art performance on specific tasks.
- Strong opinions on LLM training techniques (e.g., sequence-level vs. token-level importance ratios).
- Bachelor's, Master's, or PhD 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.
- Proficient in Python and modern ML frameworks like PyTorch or JAX.
- Solid understanding of LLM post-training techniques (supervised fine-tuning, reinforcement learning, on-policy distillation).
- Experience developing, evaluating, and deploying ML models in production.
- Strong research and problem-solving skills, adaptable to fast-moving environments.
- Publications, open-source contributions, or demonstrated research impact in LLM post-training or agent learning (preferred).
- Experience with distributed training, GPU acceleration, and large-scale model training systems (preferred).
- Experience leading technically complex projects or mentoring engineers (preferred).