Senior AI Product Manager, LLM-Powered B2B SaaS

Stanford Climate Ventures; New York, Northern (NY, KY); Hybrid; USD 175,000 - 190,000; Full time

Job summary

Stanford Climate Ventures seeks a Senior/Lead Product Manager to drive AI-powered features across workstreams, starting with an MCP integration and a customer workflow builder. You will translate real-world decarbonization challenges into scalable, measurable solutions.

You will collaborate across customer success, sales, and partnerships, building a shared AI infrastructure and using data to guide decisions, including KPIs like model quality and latency.

Qualifications

• 5+ years of product management experience, ideally in B2B SaaS.
• 2-3 years shipping LLM-powered features to customers.
• Strong leadership to influence cross-functional teams.
• Excellent communication and presentation skills.
• Data-driven decision making with KPIs and metrics.
• Team player with collaborative working style.

Responsibilities

• Translate decarbonization/building management challenges into scalable solutions delivering measurable value.
• Prioritize ruthlessly to balance long-term growth with near-term impact.
• Collaborate across customer success, sales, and partnerships for roadmap alignment.
• Build shared AI infrastructure to enable other teams to ship features.
• Engage with customers to inform product design and ensure a delightful user experience.
• Leverage data analytics to inform decisions and monitor KPIs (model quality, evals, latency, cost).
• Partner with GTM teams to develop and execute go-to-market strategies.

Skills

Product management

LLM-powered features

Leadership

Communication

Analytical skills

Team collaboration

Customer focus

Job description

Stanford Climate Ventures seeks a Senior/Lead Product Manager to drive AI-powered features across workstreams, starting with an MCP integration and a customer workflow builder. You will translate real-world decarbonization challenges into scalable, measurable solutions.

You will collaborate across customer success, sales, and partnerships, building a shared AI infrastructure and using data to guide decisions, including KPIs like model quality and latency.