Senior Applied ML Engineer

The CRAFT (Capabilities, Routing, Adaptation & Fine-Tuning) 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.

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.

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 CRAFT 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.

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. 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 CRAFT 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 find their own company in the future.

• Bachelor’s, Master’s, or PhD degree 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
• Strong programming skills in Python and hands on experience with modern ML frameworks such as PyTorch or JAX
• Strong understanding of LLM post-training techniques, including supervised fine-tuning, reinforcement learning, and on-policy distillation
• Experience developing, evaluating, and deploying machine learning models in production environments
• Strong research and problem solving skills, with the ability to work effectively in ambiguous, fast moving environments

• Publications, open source contributions, or demonstrated research impact in LLM post-training or agent learning
• Experience with distributed training, GPU acceleration, and large scale model training systems
• Experience leading technically complex projects or mentoring other engineers

We strive to use the best tool for the job when building and deploying our production services. Sometimes that means writing our own custom code, and often it means leaning on the work of others. As part of building Serverless RL, we depend on the following libraries and frameworks (among many others):

• Kubernetes
• Megatron
• Temporal
• Postgres
• FastAPI