Role OverviewA Principal Product Manager, AI will define strategy and drive delivery across a portfolio of AI products, leading through influence and making engineers, data scientists, designers, product managers, and business leaders more effective.
What You Will Do
The role involves defining portfolio strategy and vision, business discovery at scale, hands-on prototyping, requirements and scoping, delivery and execution, evaluation and quality standards, solution architecture, cross-organizational leadership, governed delivery, talent and standards, communication and influence, and staying current with advances in models and techniques.
Why It Might Be a Fit
The ideal candidate will have a track record of setting strategy and driving delivery across multiple products or a major domain, and of leading initiatives with org-wide, executive-level visibility, with a deep working understanding of modern AI/ML and GenAI, and experience in building evals and using precision- and recall-based metrics to make product decisions.
Requirements
- 10+ years of product or related experience, with AI/ML or GenAI products shipped to production
- Portfolio-level impact: A track record of setting strategy and driving delivery across multiple products or a major domain
- Hands-on building: You use AI tools regularly, prototype your own ideas, and can say specifically what you would change about a model's behavior and why
- Evaluation expertise: You have built evals (golden sets, ground truth, and offline and live evals) and used precision- and recall-based metrics to make product decisions
- Systems thinking: When you find a problem, you build the infrastructure that prevents the whole class of problem, and you make that infrastructure reusable across the organization
- Communication across domains: You move fluently between business and technical concepts, with a track record of business cases, ROI models, and roadmaps that inform investment decisions at the executive level
- AI knowledge: A deep working understanding of modern AI/ML and GenAI (LLMs, agents, RAG, prompt/harness engineering, and Evals) and how it applies to enterprise problems
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