Technical Product Manager

Must haves

• Product management experience owning a multi-tenant SaaS platform or content management system, where one change affects many customers with different configurations.
• Experience taking products from prototype to production, including deciding how to evaluate output that is probabilistic and imperfect.
• Experience in a mature or legacy system: migrations, deprecations, and shipping safely around existing behavior.
• Hands-on use of AI tools (e.g. Claude Code, Cursor, Figma Make, v0, Lovable, Replit) for prototyping, discovery, and understanding unfamiliar code. Show us something you built.
• Comfort with feature flags, staged rollouts, and release risk, including saying "not yet."
• Ability to turn customer and stakeholder feedback, often relayed through internal teams, into clear priorities.
• Working knowledge of privacy and accessibility obligations for software that handles minors' data (FERPA, COPPA, ADA/WCAG), or a demonstrated ability to get up to speed fast.

As a plus

• Experience with content migration, site builders or content management system products.
• K-12 or education software experience, including student information system integrations.
• Shipped LLM-powered features inside an existing product, especially where PII, moderation, or output quality was a risk.
• Ran alpha, beta, or early-access programs with contracted customers rather than self-selected enthusiasts.
• Experience with product analytics and data tooling.
• Experience working across teams that share a data model or API (web, mobile, communications).
• Experience with an AI-assisted software development lifecycle, such as writing tickets for coding agents.

Key responsibilities

• Own the roadmap for both products. Balance new capability, reliability, tech debt, and AI experimentation, and defend the mix to leadership.
• Define what "good enough" means for AI-generated output. Set quality bars for generated sites (fidelity to the source site, accessibility, acceptable manual cleanup). Decide when a human reviews and when automation ships directly.
• Prototype to learn, then write down what matters. Use AI tools to build working concepts quickly. Before engineering builds, convert them into constraints: customer-level scoping, permissions, feature flags, data contracts. Keep written decision records wherever your area meets another system, including the line between the platform and the launch tooling.
• Run staged rollouts and early-access programs. Plan customer cohorts, flags, and rollback paths. Pick launch customers and set expectations with them through our Support and Services teams.
• Work with delivery teams as internal customers. The people who launch sites are your first users. Learn how they work today, where automation helps, and where it creates rework.
• Turn feedback into direction. Synthesize input from Support, Services, and customers into priorities, and tell a loud customer from a systemic gap.
• Define and measure success. Instrument what you ship and pull your own numbers from product analytics and our data warehouse.