Principal Product Mgr I

Principal Product Manager – Leap Space, UK

Lead the engine that powers Leap Space’s AI‑assisted research workspace.

Own the product strategy, vision, and roadmap for agentic retrieval and orchestration, and drive its evolution from prototype to production.

Responsibilities

• Own the product strategy, vision, and roadmap for agentic retrieval and orchestration.
• Make sequencing decisions that span multiple teams, determining what ships first, what waits on dependencies, and the cost of each answer.
• Lead the staged migration of new engine capabilities from prototype to production, setting incremental releases and refining targets.
• Translate validated prototype behavior into engineering‑ready work—tickets with clear acceptance criteria and a technical approach refined with the team.
• Keep squad teams (UX, design, data science, and others) informed ahead of engine changes, briefing each group early to enable downstream planning.
• Support other squads’ agentic requirements by incorporating their requests into the engine backlog and consolidating shared needs into platform capabilities.
• Anticipate demand on the agentic system from the product roadmap—new sources, formats, and workflows—and position the engine accordingly.
• Expand Leap Space’s surface area by growing tools and skills, consolidating user tasks and personas, and collaborating with partner teams across Elsevier.
• Collaborate with external partners across Elsevier to develop capabilities that span products.

Requirements

• 10+ years building and shipping complex AI or data‑intensive products, with sustained ownership of strategy, trade‑offs, discovery, and delivery.
• At least three consecutive years at one employer, with experience more important than a specific title.
• Deep expertise in generative AI products, including retrieval‑augmented generation, agentic workflows, and their evaluation.
• Proven ability to independently drive product strategy and execution across multiple engineering organisations and external dependencies.
• Strong go‑to‑market experience and skill in hypothesis‑driven product evaluation, including A/B testing and experimentation frameworks.
• Comfortable with AI coding tools and rapid prototyping; able to build proofs of concept and validate ideas alongside engineering and data science teams.
• Experience in customer discovery within academic, research, or professional information markets is highly desirable.
• Technically fluent with sound architectural judgment, able to reason about system boundaries, trade‑offs, and engage with architects, AI researchers, and platform teams.

If you are interested, please apply with a cover letter and CV.

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