Sr Product Mgr II

Senior Product Manager II, Knowledge Management & AI 

Do you have at least 8+ years of product management experience?

Do you have a wealth of experience with AI/ML products, especially generative AI, RAG, or agentic workflows?

Join us to shape how researchers build on their own knowledge with AI. 

Location: UK (London) or Amsterdam. Remote-first with periodic in-office collaboration. 

About our Team 

We build LeapSpace, an AI-assisted workspace where researchers find scientific literature they can trust and do their best thinking in one place. We’re an international team and we work from evidence. The aim is simple: researchers should spend less time wrangling information and more time thinking.  Own knowledge management and AI at LeapSpace, our AI-assisted research workspace: the product's memory of a researcher's projects, the personalisation around it, and how the AI reasons over the knowledge researchers bring with them and build up over time. The long-term ambition is a living, AI-maintained knowledge base for every researcher, evolving toward goal-oriented research the system can pursue iteratively. The role pairs strategy with delivery: you set that direction and ship it with your own squad, whose current focus is project spaces. We're looking for a Senior Product Manager with deep product judgment, hands-on experience with AI/ML products (generative AI, RAG, or agentic workflows), and the instinct to work from customer evidence alongside data science. UK (London) or Amsterdam; remote-first with periodic in-office collaboration. 

About the Role 

We're looking for a Senior Product Manager with deep product judgment to own the strategy for knowledge management and AI at LeapSpace. 

This is a strategy-first role, but a pragmatic one. Researchers don't start from a blank page. They come with papers they've saved, drafts they've written, and questions they've chased for months. Your job is to make LeapSpace reason across that personal knowledge as well as the wider scientific corpus, and to turn today's scattered features into one experience that gets more useful the longer someone uses it. 

The destination is ambitious. Think of a living knowledge base that the AI builds and maintains with the researcher: structured, current, and grown from every project, source, and finding they work through. From there the work points toward goal-oriented research, where a researcher states what they are trying to achieve and LeapSpace runs iterative research loops against that goal, drawing on everything it already knows about their work. You will define the staged path from today's features to that destination, and make each stage earn its keep. 

The strategy ships through a real squad. You'll work in a team building project spaces: the workspace where a researcher's questions, documents, and findings live together, and the foundation the personal-knowledge experience grows from. You'll be in the detail with your engineers sprint by sprint. Beyond that, the near-term work is the product's memory of a researcher's projects, the personalisation around it, and the tools they use to keep and reuse what they find. It also covers how LeapSpace reads the documents a researcher brings in, though upload and storage themselves sit with a partner team you'll work with closely. 

You'll report and work alongside the Senior Director of Product Management and Strategy for LeapSpace. Day to day you'll work with a full-stack engineering team and an embedded data science team, and partner with the team that owns core retrieval. We want PMs who stay close to the technology and can reason about how the AI behaves, how it's evaluated, and where the trade-offs lie. You'll prototype with AI tools as you grow into that, but your craft is product management, not engineering. 

This field moves quickly, and we expect genuine curiosity about it. The right person follows new ideas, techniques, and technologies as they emerge, tests them independently through their own research, and brings informed opinions to decisions rather than waiting for the team to evaluate everything first. 

Responsibilities 

• Own the product strategy, vision, and roadmap for knowledge management and AI. The near-term features are how that strategy shows up, not the strategy itself. 

• Lead delivery of project spaces with your squad, and the wider current surface: the project memory system, the personalisation around it, and the tools researchers use to keep and reuse their work. 

• Run rigorous customer discovery into how researchers actually manage and build on their knowledge, and turn it into product decisions you can defend. 

• Use analytics and hypothesis-driven experimentation to make evidence the team's default way of deciding. 

• Anticipate what the product roadmap will ask of the personal-knowledge layer (new project workflows, collaboration, richer integrations) and keep the strategy ahead of it. 

• Partner with the team that owns document upload and storage on how LeapSpace surfaces and reasons over uploaded documents. Upload and storage functionality stays with that team.

• Partner with the team that owns core retrieval on how the AI reasons across a researcher's knowledge and the wider corpus. 

• Work with embedded data scientists to evaluate response accuracy, relevance, and trust. Commission the evaluations, read the results, and turn them into prioritised improvements.