Principal Product Manager - AI Dublin​/Prague

United Kingdom - Remote

Locations:
London / Dublin / Prague

At Productboard, we're building the first agentic product management system. We're rethinking how product management is done in a world full of AI agents, and how we help our customers build what matters for their audience.

Every product organization sits on a mountain of raw signal: customer feedback, research, support conversations, sales notes, usage metrics, market data. A single customer can process hundreds of thousands of signals a year,
and almost none of it turns into decisions. Spark, our AI agent for product teams, exists to change that: to turn that volume into the handful of insights behind the decisions a product leader has to make and defend. We're looking for a Principal Product Manager to own that opportunity end to end: a hands‑on senior IC from an AI-first product company who owns the pipeline that turns raw signals into reliable insights, the agent behavior that reasons over them, and the quality of the answers and artifacts Spark produces, and who partners directly with our founders on the hardest problems along the way.

Our users are product makers themselves. They know what good product management looks like, and they'll judge our agents on whether they make theirs better.

Know the customer better than anyone. Understand the questions product leaders most need answered, how modern product organizations actually work, and where an agent removes work rather than adding tooling. Bring that to every prioritization call.

Own the AI Insights roadmap. Set the strategy, roadmap, and metrics for the domain. Decide which questions Spark answers first, what a great answer looks like for each, and the quality bar it meets before it ships. Lead the hardest problems in the domain, like telling a feature gap from a use case we've never served, or discovering segments the CRM has no field for.

Own the path from signal to decision. Decide what data and context Spark needs to answer reliably, how it flows through the data model, retrieval, agent harness, and experience, and whether a weak answer gets fixed through better data, retrieval, prompting, tooling, model behavior, or UX. Decide what belongs in Insights and what belongs in the shared AI platform.

Define how agents behave and how answers show up. Decide what Spark does in each context, how it handles uncertainty, and when it asks for help. Decide when an answer is conversational, structured, visual, or fully interactive, and lead the move from text outputs to rich, interactive artifacts.

Own quality and evals. Write the evals that define a good answer, insight, or artifact, and codify them so engineering has something precise to build against. Read the traces yourself, watch for regressions, and make sure every release makes Spark measurably better.

Anticipate the frontier. Track what the next generation of models and agent frameworks makes possible, position Spark to capture it first, and give GTM teams a crisp articulation of what the agents can and cannot do.

Prototype in code by default. When words aren't settling a debate, build the agent with AI coding tools and put it in front of customers.

Raise the bar. Be the first user of Spark for your own work, and mentor other PMs on AI product craft through pairing and example.

8+ years in product management, all of it hands‑on. A senior individual contributor on complex products, with meaningful time in enterprise B2B SaaS, direct ownership of an AI or LLM‑powered product in production, and an AI product that is visibly better because you owned it.

Deep expertise in designing and developing agentic AI products. You've shipped products…