Senior Software Engineer II, Applied Training

CoreWeave•Sunnyvale, CA

7d•$182,000 - $242,000•Onsite

About The Position

CoreWeave is seeking a Senior Software Engineer II for the Applied Training team. This role focuses on solving the problem of customers spending valuable research time on cluster setup and operations instead of AI model training. The Applied Training team aims to provide every CoreWeave customer with research infrastructure comparable to that found in frontier labs. As an early member of this small team, you will be responsible for our Kubernetes-native research cluster platform, the sandbox client for agentic training and evaluation, or potentially a new project. The goal is to give customers the research infrastructure they need to succeed. The role involves contributing to the team's roadmap, working closely with customers and internal teams building cloud-native primitives. Specific responsibilities may include designing and building a complete research cluster experience (CLI, job configuration schema, Kubernetes operators, daemons) to address researcher challenges like code distribution, checkpoint-triggered evaluation, cross-cluster scheduling, and programmatic job control. Alternatively, you might own the Python SDK for sandbox infrastructure, enabling large-scale RL training runs with isolated containers. You will also write documentation for running popular OSS training frameworks on CoreWeave and collaborate with infrastructure teams and customers (large AI labs with thousands of GPUs) to understand and integrate their supercomputing stack knowledge into our offerings.

Requirements

8–12+ years building distributed systems, ML infrastructure, or developer platforms
Real Kubernetes experience: custom controllers, operators, scheduling, CRDs, workload orchestration at scale — not just deploying things to Kubernetes or cluster administration
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
Strong communicator who can work with customers and translate researcher complaints into system designs

Nice To Haves

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

Responsibilities

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