Senior AI Product Manager

About us

Ruby Labs is a leading tech company that creates and operates innovative consumer products. We offer a diverse range of opportunities across the health, education, and entertainment industries. Our innovative teams are driving the future of consumer-led products, and we're always looking for passionate individuals to join us. Learn more about our story at: https://rubylabs.com/about-us/

About the role

We are looking for an exceptional Senior AI Product Manager to own the AI product direction for one of our core D2C subscription products. You will be fully responsible for the AI surface end-to-end — defining the vision, shaping the roadmap, specifying features, driving development with engineering, and owning the results in production. The product serves hundreds of thousands of customers, and AI-powered flows are the primary growth lever across a portfolio of surfaces spanning document creation, editing, conversion, and automation workflows — including underscaled surfaces with significant untapped potential.

This is a high-ownership role suited for someone who operates from vision down to shipped detail — not a backlog administrator. You will work alongside an existing product owner and take genuine end-to-end ownership: what to build, why, how it should work, and whether it worked. Ambiguity is the default; you are expected to bring clarity, ship fast, and hold a very high bar for user experience. Every significant decision here is made on data — you pull it yourself, read it correctly, and let it overrule opinion, including your own. The bar is high and so is the upside — performance here is directly and significantly rewarded, and strong operators take on bigger scope fast.

Key Responsibilities

AI Vision & Strategy

• Own the AI product vision and roadmap — where AI creates real customer value versus where it is noise

• Define which AI-powered flows to build, scale, or kill, grounded in usage data and business impact

• Identify underscaled product surfaces and build the case, plan, and execution path to grow them

• Track the AI landscape (models, tooling, competitor moves) and translate it into product advantage

Feature Definition & Delivery

• Translate vision into clear specifications: user flows, edge cases, quality bars, and success metrics

• Drive end-to-end delivery with engineering and design — from concept through QA to production

• Use AI tools daily (Claude and similar) to prototype concepts that are directly considered for production

• Own launch decisions: ship, iterate, or retire — based on data, not attachment

Experimentation & Product Quality

• Build