Senior Technical Product Manager

About Staffbase We inspire people to achieve great things together. Our mission is to help organizations unlock the power of inspirational communication with the first AI-native Employee Experience Platform . Our industry-leading and award-winning agentic AI communications channels - intranet, employee app and email solutions - create engaging experiences that connect and empower employees. Headquartered in Chemnitz, Germany and New York City, with offices in Berlin, London, Sydney, Tokyo, Prague, and Minneapolis–St. Paul, our diverse team of 750+ employees supports 2,000+ customers—reaching over 16.4 million employees—in transforming their employee experience. We are proud to be a Unicorn company—privately valued at over $1 billion—demonstrating strong growth, innovation, and lasting impact in our industry. Together, we’re shaping the future of workplace communication. At Staffbase, the search experience is one of the most strategically important problems we’re solving. Our stream is responsible for two deeply connected products: Hybrid Search — our next-generation search intelligence combining keyword and semantic search — and Navigator, the AI assistant that employees interact with directly. We’re looking for a Senior Technical Product Manager to own Hybrid Search end-to-end: from our upcoming GA release through the next stage of expansion into enterprise-grade, multi-platform search intelligence. This is one of the top initiatives in the company, with high visibility to senior leadership and a meaningful impact on how millions of frontline employees find the information they need — whether they’re at a desk or on the go as a nurse, technician, or field worker. This is a role for someone who finds depth and craft in growing and maturing a technically complex product, negotiating priorities with a senior engineering team, and continuously keeping the end user front and centre even when the work gets deeply technical. What you’ll be doing Own the transition from GA release to the next phase — incorporating early customer feedback, defining what gets fixed vs. what gets expanded, and driving a clear prioritization call on roadmap direction Establish the right success metrics for search: balancing technical performance signals with genuine user value Work closely with the AI Assistant team to ensure search intelligence keeps pace with what the assistant surfaces Expand search coverage: video subtitle indexing, richer content types, deeper platform integrations (SharePoint, Confluence, and beyond) Build toward enterprise search: connecting Staffbase to the knowledge bases customers already use Drive re-ranking improvements and relevance quality as the product matures Help define what it means for an employee to find the right information, at the right time, from any device What you need to be successful 5+ years of product management experience, ideally with exposure to search, information retrieval, or AI/ML-powered products B2B SaaS background strongly preferred — you understand enterprise customers, IT stakeholders, and the complexity of integrations Demonstrated ability to work on technically complex products while keeping user needs visible and central You find satisfaction in iterating, improving, and maturing a product, not just launching it Confident and opinionated: you can hold your own with a senior, highly technical engineering team and negotiate priorities accordingly Technically fluent: you don’t need to write code, but you need to understand how search infrastructure, indexing, and ranking work well enough to have credible conversations about trade-offs User-oriented: even on a deeply technical product, you know how to tie performance metrics back to what actually matters for the end user Highly desirable Experience with semantic or vector search — you understand embeddings, retrieval models, and the difference between keyword and meaning-based matching Familiarity with search relevance and re-ranking — you know what it takes to tune results quality and can have an informed conversation about signals, scoring, and trade-offs Experience working with indexing pipelines — understanding how content gets ingested, structured, and made searchable Exposure to enterprise knowledge integrations — connecting a search layer to external platforms like SharePoint, Confluence, or similar knowledge bases Awareness of AI-generated summaries or answer synthesis — you’ve worked on or alongside products that generate a direct answer from retrieved results Familiarity with search evaluation frameworks — you’ve thought about offline evaluation, human judgment, and metric design beyond just click-through rate What you'll get Competitive