Senior Technical Product Manager
HybridChemnitz, Saxony, Germany
Job Summary
Own the transition of Hybrid Search from GA release to enterprise-grade expansion, defining prioritization based on customer feedback and balancing technical performance with user value. Establish success metrics for search intelligence, drive re-ranking improvements, and expand coverage to include video subtitles and integrations with SharePoint and Confluence. Collaborate with the AI Assistant team to ensure search aligns with surfacing capabilities and help define how employees locate information across devices. Manage roadmap direction for multi-platform search intelligence while maintaining credibility with senior engineering teams on infrastructure and indexing trade-offs.
Required Qualifications
- 5+ years of product management experience
- Demonstrated ability to work on technically complex products while keeping user needs visible and central
- 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
Desired Qualifications
- B2B SaaS background strongly preferred — you understand enterprise customers, IT stakeholders, and the complexity of integrations
- You find satisfaction in iterating, improving, and maturing a product, not just launching it
- 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
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