# Staff Product Manager, Training Infrastructure - Weights & Biases **Company**: CoreWeave **Location**: Livingston, NJ / New York, NY / San Francisco, CA / Sunnyvale, CA / Bellevue, WA **Experience**: staff **Job type**: full-time **Salary**: $188,000 to $275,000 **Category**: Engineering **Industry**: Technology **Apply**: https://job-boards.greenhouse.io/coreweave/jobs/4639868006?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_a902ff10-b17 ## Description 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. About the Role Training a frontier model is as much an infrastructure problem as it is a modeling problem. When a run fails at hour 47 of a 72-hour job, the question is almost never "what's wrong with the code" , it's "what happened on the cluster." Node failures, network timeouts, storage bottlenecks, and scheduling decisions determine whether training runs succeed or fail, how much they cost, and how fast teams can iterate. Until now, the developer tools and the infrastructure telemetry have lived in completely separate worlds. The CoreWeave acquisition changes that. W&B can now build products that connect application-level experiment tracking with cluster health, hardware telemetry, and compute orchestration , a surface area that's hard to assemble without owning both the developer tools and the infrastructure. Reporting to the Director of Product Management, this Staff Product Manager will own the strategy and execution for the products at this intersection: W&B Launch (job submission and compute orchestration), infrastructure insights that leverage CoreWeave's unique telemetry and platform capabilities, and new product concepts , including agentic and automated research workflows , that only exist because W&B and CoreWeave are now one company. Sandboxes (managed development environments) are also part of this surface, but the core opportunity is building infrastructure-native developer tools that take advantage of the combined platform. You'll be working directly with frontier model builders , teams training on thousands of GPUs , to understand what they need and ship it. If you're energized by solving infrastructure challenges that directly accelerate deep learning workflows, collaborating with the world’s best AI teams, and delivering tools that move the industry frontier forward, this role is for you. Responsibilities: - Define and ship infrastructure-native products that only W&B + CoreWeave can build. - Own W&B Launch and evolve it for the CoreWeave era. - Spend real time with frontier AI teams and learn how they actually work. - Build the bridge between W&B product and CoreWeave platform engineering. - Find and resolve friction points in the job submission, compute management, and infrastructure observability workflows. - Explore new product concepts at the hardware-software boundary. Requirements: - Experience: You have 7+ years as a product manager working on developer tools, deep learning infrastructure, or ML engineer platforms. - End-to-end ownership: You've taken a technical product,whether a model, orchestration feature, or developer tool,all the way to production. - Technical fluency: You’re comfortable discussing Kubernetes and SLURM, job scheduling, hyperparameter optimization algorithms, and API design with engineers and AI researchers. - Cross-functional influence: You’re skilled at managing and influencing AI engineers, software engineers, and stakeholders. - Clear communication: A major part of this role is seeking feedback from many directions and channeling it into a single, executable stream. Preferred Qualifications: - Experience with deep learning frameworks (PyTorch, JAX), distributed training, or LLM development workflows. - Hands-on experience with SLURM or Kubernetes for GPU-intensive workloads. - Prior exposure to W&B or other experiment-tracking and model management platforms. - Understanding of hyperparameter optimization methods and evaluation workflows for large models. What We Offer - Base salary range: $188,000 to $275,000 - Discretionary bonus - Equity awards - Comprehensive benefits program ## Skills ### Required - Kubernetes - SLURM - job scheduling - hyperparameter optimization - API design - deep learning infrastructure - ML engineer platforms ### Nice to have - PyTorch - JAX - distributed training - LLM development workflows - W&B --- Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/coreweave/jobs/4639868006?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)