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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer II, Applied Training - **Company:** Coreweave Inc - **Location:** Bellevue, WA, United States - **Experience:** Expert - **Salary:** $182,000.0 - $242,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Daemon Tools, Distributed Systems, Python (Programming Language), Automation of Marketing, Azure Machine Learning, Graphics Processing Unit (GPU), Pytorch, Backend, Build Management, Kubernetes, Slurm, Machine Learning Operations, Serverless Computing - **Published:** October 2, 2026 - **Apply:** https://www.dice.com/job-detail/b3ed675b-d47a-4052-bb80-c1ef105d6ae0 ## About the Role * 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 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, 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 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. ## Description 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. In this role, you will: * 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 ## Related Videos - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)