Applied ML Engineer

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.

• 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

• 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

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

We work hard, have fun, and move fast! We’re in an exciting stage of hypergrowth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop.