Senior Software Engineer (Applied Training)
Posted Updated
- 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
- Contribute to the roadmap for Applied Training — figure out what actually unlocks new workloads and what’s just nice to have
- Work directly and closely with customers, and other teams inside CoreWeave that are building cloud native primitives: compute, storage, networking, etc
- For the research cluster platform: design and build a complete research cluster experience — CLI, job configuration schema, Kubernetes operators, daemons — solving the problems researchers actually hit: code distribution, checkpoint-triggered evaluation, cross-cluster scheduling, programmatic job control
- For sandbox infrastructure: own the Python SDK and work in a tight loop with the backend team, enabling RL training runs to spawn thousands of isolated containers for agent rollouts and agent benchmarks at scale
- Write documentation for running popular OSS training frameworks on CoreWeave to unblock customers and help them succeed
- Work with infrastructure teams and customers directly — the customers are large AI labs running thousands of GPUs; understand how they structure their internal supercomputing stacks and bring that knowledge back to what we build
- 8–12+ years building distributed systems, ML infrastructure, or developer platforms
- You understand what makes researchers productive: code distribution matters, fast iteration cycles matter, workflows that don’t require becoming infrastructure experts matter
- Familiarity 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
- Real Kubernetes experience: custom controllers, operators, scheduling, CRDs, workload orchestration at scale — not just deploying things to Kubernetes or cluster administration
- Strong communicator who can work with customers and translate researcher complaints into system designs
- Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren’t a 100% skill or experience match
- 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, and opinions on where they fall short
- Experience with container runtimes, isolation (gVisor, Kata), or serverless platforms
- OSS contributions to Kubernetes SIGs, Ray, PyTorch, or similar