Senior Product Manager, Context Engineering

ZoomInfo ZoomInfo · Enterprise · Waltham, MA · 942 Product Management - Data Management

Senior Product Manager for Context Engineering at ZoomInfo, focusing on architecting and owning the context layer for AI intelligence across various products. This role involves designing acquisition pipelines, building a platform for internal customers, driving quality through evaluation frameworks, navigating emerging research, and orchestrating cross-functional execution. Requires experience in ML/AI infrastructure, RAG systems, vector databases, and platform products.

What you'd actually do

  1. Architect Context Acquisition Pipelines Design and optimize how ZoomInfo retrieves, transforms, and delivers context from our semantic data layer, memory systems, and data producers. You'll balance retrieval quality against latency and cost constraints, implementing hybrid search strategies, intelligent caching, and context compression techniques that maintain information density while respecting token budgets.
  2. Own the Context Layer Platform Build infrastructure serving multiple product teams—Copilot, GTM Studio, MarketingOS—as internal customers. Establish API contracts, developer experience standards, and integration patterns that accelerate feature velocity. Maintain the delicate balance between providing flexible building blocks and opinionated solutions that encode best practices.
  3. Drive Quality Through Measurement Implement evaluation frameworks using RAGAS metrics and custom benchmarks. Monitor retrieval precision, context relevance, hallucination rates, and system performance in production. Translate quality signals into architectural improvements, working closely with ML engineers to iterate on embedding models, reranking strategies, and retrieval algorithms.
  4. Navigate Emerging Research Context engineering evolves weekly. You'll continuously evaluate innovations—GraphRAG for multi-hop reasoning, test-time compute scaling, multimodal retrieval, compression techniques—determining which advances warrant production investment versus which remain academic curiosities. Bring external best practices to ZoomInfo while contributing learnings back to the broader community.
  5. Orchestrate Cross-Functional Execution Translate between three distinct worlds: ML engineers optimizing retrieval algorithms, platform engineers building scalable infrastructure, and product teams shipping customer features. Establish communication cadences, prioritization frameworks, and decision-making processes that balance urgent requests against strategic platform development.

Skills

Required

  • Product management experience
  • ML/AI infrastructure experience
  • Production RAG systems experience
  • Vector databases experience
  • Semantic search experience
  • Context management experience
  • Graph databases experience
  • Platform products experience
  • Context compression knowledge
  • Embedding models knowledge
  • Retrieval evaluation frameworks knowledge
  • Python proficiency
  • SQL proficiency

Nice to have

  • Experience with Neo4j
  • Familiarity with multimodal retrieval
  • Familiarity with test-time compute scaling

What the JD emphasized

  • 6-8 years of product management experience with 2+ years in ML/AI infrastructure
  • Direct experience with production RAG systems, vector databases, or semantic search, context management
  • Track record building platform products serving multiple internal or external customers
  • Expert-level understanding of RAG system architecture
  • You've built or significantly contributed to production retrieval systems, not just managed them at arm's length.
  • Python and SQL proficiency enables you to review code, analyze retrieval issues, and prototype solutions for concept validation.
  • Experience building infrastructure products where internal engineering teams are your customers.
  • You measure success through downstream product velocity improvements and developer satisfaction scores, not just uptime metrics.
  • You understand platform economics
  • You read recent research papers from arXiv, ACL, NeurIPS.
  • You prototype emerging techniques to understand their practical constraints.
  • continuous learning is non-negotiable.
  • You translate between technical depth and business impact fluently.
  • You can explain to executives why implementing GraphRAG takes 6 months but unlocks $10M in product capabilities.
  • You can communicate to engineers why business constraints require shipping "good enough" in 3 weeks rather than "optimal" in 3 months.
  • You influence without formal authority through data, clear reasoning, and earned credibility.

Other signals

  • AI-first product thinking company-wide
  • Context engineering is the emerging discipline that determines whether AI systems deliver transformative value or incremental improvement
  • The context pipelines exist but remain nascent—creating a rare opportunity to define architectural patterns and platform standards that compound value across multiple product teams in the years to come.