Senior Product Manager, Context Engineering

Overview

Zoom Info is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.

The Opportunity

Zoom Info built the industry's most sophisticated GTM data acquisition infrastructure. Now we're applying that same rigor to context engineering—the emerging discipline that determines whether AI systems deliver transformative value or incremental improvement.

This role architects the context layer powering our AI intelligence across Copilot, GTM Studio, and Marketing

OS. You'll transform how Zoom Info's agentic workflows access, compress, and deliver precisely the right information at exactly the right moment. The impact is organization-wide: every AI interaction, every intelligent recommendation, every autonomous agent action depends on the context infrastructure you’ll build.

We've transitioned to AI-first product thinking company-wide. 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.

• Architect Context Acquisition Pipelines Design and optimize how Zoom Info 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.
• Own the Context Layer Platform Build infrastructure serving multiple product teams—Copilot, GTM Studio, Marketing
  
  OS—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.
• 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.
• Navigate Emerging Research Context engineering evolves weekly. You'll continuously evaluate innovations—Graph
  
  RAG 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 Zoom Info while contributing learnings back to the broader community.
• 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.

• 4-6 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
• Experience with graph databases (e.g. Neo4j)
• Track record building platform products serving multiple internal or external customers
• Familiarity with context compression, embedding models, and retrieval evaluation frameworks
• History of defining product vision in nascent technical domains where best practices are still emerging

• Technical Foundation Expert-level understanding of RAG system architecture—you can discuss embedding dimensionality trade-offs, vector database indexing strategies, and reranking approaches with depth. You've built or significantly contributed to production retrieval systems, not just managed them at arm's length.…