Director, AI Product

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What the market looks like

We've tracked 3,400+ senior-level product postings in the United States over the last six months. Hiring concentrates in technology hubs—New York, San Francisco, and Texas—and spans financial services, technology, and professional services firms. Directors in this space typically command $240,000–$390,000 annually. The strongest candidates combine deep product strategy experience with hands-on fluency in AI/ML capabilities, ship-track records in fast-moving environments, and the ability to translate technical complexity into clear customer value and business outcomes.

Job responsibilities

• Own end-to-end product strategy for AI-powered features or capabilities—from discovery and roadmap definition through launch and iteration—balancing user needs, technical feasibility, and business impact.

• Lead cross-functional teams (engineering, design, data science, go-to-market) to ship AI products and integrations, defining success metrics and driving accountability for outcomes.

• Build and communicate the product vision for AI capabilities; translate technical possibilities into customer-centric use cases and positioning.

• Partner with senior leadership and customers to validate product-market fit; conduct research, run experiments, and iterate based on real-world feedback.

• Develop and mentor mid-level product managers and individual contributors; build a culture of ownership and rigorous thinking within the product organization.

• Manage product economics—roadmap prioritization, resource allocation, and ROI modeling—to ensure AI investments drive sustainable competitive advantage.

• Stay current with AI/ML advances and competitive landscape; advise leadership on emerging capabilities and their implications for product strategy.

Candidate requirements

• 8+ years of product management experience, with at least 3 years shipping AI or ML-powered features at scale; track record of taking products from concept to revenue or adoption.

• Working fluency in AI/ML fundamentals (LLMs, prompt engineering, RAG, fine-tuning, evaluation frameworks); ability to read a technical spec and collaborate effectively with ML engineers and data scientists.

• Demonstrated leadership of cross-functional teams and ability to influence without formal authority; experience operating in ambiguity and setting direction in emerging technology areas.

• Strong communication and storytelling skills; ability to move between highly technical and non-technical audiences and make complex concepts clear.

• Analytical rigor and customer obsession; experience running experiments, analyzing data, and making decisions grounded in evidence rather than intuition.

• Comfort with rapid change and willingness to challenge your own assumptions; experience in high-growth, fast-moving, or startup-like environments.