Staff Product Manager, Training Infrastructure - Weights & Biases

About the role

The Staff Product Manager will own the strategy and execution for products that bridge W&B's experiment tracking with CoreWeave's infrastructure platform. This includes defining and shipping infrastructure-native products, evolving W&B Launch, and exploring new product concepts.

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: 7+ years as a product manager working on developer tools, deep learning infrastructure, or ML engineer platforms.
• End-to-end ownership: Take a technical product all the way to production, including defining success criteria, measuring effectiveness, and iterating to improve impact and user happiness.
• Technical fluency: Comfortable discussing Kubernetes and SLURM, job scheduling, hyperparameter optimization algorithms, and API design with engineers and AI researchers.
• Cross-functional influence: Manage and influence AI engineers, software engineers, and stakeholders.
• Clear communication: Clarify complex, fast-moving environments and organize feedback into executable streams.
• Care and taste: Strong opinions about products you use and a high bar for developer experience.

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.