Staff Software Engineer, Applied Training

CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com. What You’ll Do: A lab signs with CoreWeave. They have thousands of GPUs waiting. Their first month? Spent on cluster setup instead of research. Building deployment scripts, fighting container images, wiring up job orchestration. They came to train models. Instead, they're doing operations. This is the problem. We're building the Applied Training team to fix it. You'll be an early member of a small team, responsible for our Kubernetes-native research cluster platform, or the sandbox client for agentic training and evaluation, or possibly a new project altogether. The goal is specific: Give every CoreWeave customer the research infrastructure that currently only exists inside frontier labs. About the role: Contribute to the roadmap for Applied Training. Figure out what actually unlocks new workloads and what's just nice to have. Work directly and closel [... source excerpt omitted ...] lve the problems researchers actually hit: code distribution, checkpoint-triggered evaluation, cross-cluster scheduling, programmatic job control. Replace the patchwork of scripts customers keep building on their own. For sandbox infrastructure: own the Python SDK and work in a very tight loop with the backend team. When an RL training run needs to spawn thousands of isolated containers for agent rollouts, that's this system. When someone wants to run agent benchmarks at scale, that's this system. Make it work with our Kubernetes clusters, storage, and auth so researchers don't have to think about infrastructure. Write the documentation for running popular OSS training framewor [... source excerpt omitted ...] rity with training: how distributed jobs get scheduled, how ranks initialize, what breaks at scale. You've shipped infrastructure that other people rely on daily. Not prototypes. Production systems. Good communicator. Can work with customers, translate researcher complaints into system designs. Preferred: Experience building internal ML platforms or research clusters at a company doing large-scale training. Familiarity with agentic AI: RL training with rollouts, agent evaluation, sandbox isolation for running untrusted code. Background with Slurm, Ray, or similar workload orchestration. Opinions on where they fall short. Experience with container runtimes, isolation (gVisor,