{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/context-compression"},"x-facet":{"type":"skill","slug":"context-compression","display":"Context Compression","count":1},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_77ff2013-8f9"},"title":"Senior Product Manager, Context Engineering","description":"<p>ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. As a Senior Product Manager, Context Engineering, you&#39;ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins.</p>\n<p>With tools that amplify your impact and a culture that backs your ambition, you won&#39;t just contribute. You&#39;ll make things happen–fast.</p>\n<p><strong>The Opportunity:</strong></p>\n<p>ZoomInfo built the industry&#39;s most sophisticated GTM data acquisition infrastructure. Now we&#39;re applying that same rigor to context engineering,the emerging discipline that determines whether AI systems deliver transformative value or incremental improvement.</p>\n<p>This role architects the context layer powering our AI intelligence across Copilot, GTM Studio, and MarketingOS. You&#39;ll transform how ZoomInfo&#39;s agentic workflows access, compress, and deliver precisely the right information at exactly the right moment.</p>\n<p>The impact is organization-wide: every AI interaction, every intelligent recommendation, every autonomous agent action depends on the context infrastructure you’ll build.</p>\n<p>We&#39;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.</p>\n<p><strong>What You&#39;ll Do:</strong></p>\n<p>Architect Context Acquisition Pipelines</p>\n<p>Design and optimize how ZoomInfo retrieves, transforms, and delivers context from our semantic data layer, memory systems, and data producers. You&#39;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.</p>\n<p>Own the Context Layer Platform</p>\n<p>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.</p>\n<p>Maintain the delicate balance between providing flexible building blocks and opinionated solutions that encode best practices.</p>\n<p>Drive Quality Through Measurement</p>\n<p>Implement evaluation frameworks using RAGAS metrics and custom benchmarks. Monitor retrieval precision, context relevance, hallucination rates, and system performance in production.</p>\n<p>Translate quality signals into architectural improvements, working closely with ML engineers to iterate on embedding models, reranking strategies, and retrieval algorithms.</p>\n<p>Navigate Emerging Research</p>\n<p>Context engineering evolves weekly. You&#39;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.</p>\n<p>Bring external best practices to ZoomInfo while contributing learnings back to the broader community.</p>\n<p>Orchestrate Cross-Functional Execution</p>\n<p>Translate between three distinct worlds: ML engineers optimizing retrieval algorithms, platform engineers building scalable infrastructure, and product teams shipping customer features.</p>\n<p>Establish communication cadences, prioritization frameworks, and decision-making processes that balance urgent requests against strategic platform development.</p>\n<p><strong>What You’ll Bring:</strong></p>\n<ul>\n<li>4-6 years of product management experience with 2+ years in ML/AI infrastructure</li>\n</ul>\n<ul>\n<li>Direct experience with production RAG systems, vector databases, or semantic search, context management</li>\n</ul>\n<ul>\n<li>Experience with graph databases (e.g. Neo4j)</li>\n</ul>\n<ul>\n<li>Track record building platform products serving multiple internal or external customers</li>\n</ul>\n<ul>\n<li>Familiarity with context compression, embedding models, and retrieval evaluation frameworks</li>\n</ul>\n<ul>\n<li>History of defining product vision in nascent technical domains where best practices are still emerging</li>\n</ul>\n<p><strong>Who You Are:</strong></p>\n<p>Technical Foundation</p>\n<p>Expert-level understanding of RAG system architecture,you can discuss embedding dimensionality trade-offs, vector database indexing strategies, and reranking approaches with depth.</p>\n<p>You&#39;ve built or significantly contributed to production retrieval systems, not just managed them at arm&#39;s length.</p>\n<p>Python and SQL proficiency enables you to review code, analyze retrieval issues, and prototype solutions for concept validation.</p>\n<p>Platform Product Mindset</p>\n<p>Experience building infrastructure products where internal engineering teams are your customers.</p>\n<p>You measure success through downstream product velocity improvements and developer satisfaction scores, not just uptime metrics.</p>\n<p>You understand platform economics,how each additional team using your infrastructure increases its value through shared learnings and amortized costs.</p>\n<p>Intellectual Velocity</p>\n<p>You read recent research papers from arXiv, ACL, NeurIPS.</p>\n<p>You prototype emerging techniques to understand their practical constraints.</p>\n<p>You maintain strong opinions weakly held, updating your architectural assumptions as evidence accumulates.</p>\n<p>The discipline moves too fast for static expertise,continuous learning is non-negotiable.</p>\n<p>Strategic Communication</p>\n<p>You translate between technical depth and business impact fluently.</p>\n<p>You can explain to executives why implementing GraphRAG takes 6 months but unlocks $10M in product capabilities.</p>\n<p>You can communicate to engineers why business constraints require shipping &#39;good enough&#39; in 3 weeks rather than &#39;optimal&#39; in 3 months.</p>\n<p>You influence without formal authority through data, clear reasoning, and earned credibility.</p>\n<p><strong>The Environment:</strong></p>\n<p>Reporting &amp; Collaboration</p>\n<p>Report to the Senior Product Director for Context Engineering, Semantic Data Layer, and Agentic Memory within ZoomInfo&#39;s Intelligence team.</p>\n<p>Work alongside PMs responsible for signals and ML scoring/recommendation models.</p>\n<p>Together, you ensure our agentic workflows fill context windows with high-quality, information-dense content exactly when needed.</p>\n<p>Pace &amp; Problems</p>\n<p>Fast-moving engineering team that understands the space.</p>\n<p>Company-wide AI adoption push creates both urgency and opportunity.</p>\n<p>Expect interesting problems: How do we maintain sub-200ms retrieval latency at scale?</p>\n<p>When does GraphRAG justify its indexing cost?</p>\n<p>How do we balance context freshness with cache efficiency?</p>\n<p>You&#39;ll shape answers that become architectural patterns across the organization.</p>\n<p>Impact</p>\n<p>Define a nascent discipline at a company that&#39;s already AI-first in product thinking and organizational structure.</p>\n<p>Your architectural decisions compound,every improvement to context quality multiplies across Copilot, GTM Studio, MarketingOS, and future products we haven&#39;t imagined yet.</p>\n<p>This is infrastructure work with direct line-of-sight to customer value.</p>\n<p>#LI-PS1 #LI-remote</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_77ff2013-8f9","directApply":true,"hiringOrganization":{"@type":"Organization","name":"ZoomInfo","sameAs":"https://www.zoominfo.com/","logo":"https://logos.yubhub.co/zoominfo.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/zoominfo/jobs/8206116002","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$89,200-$133,800 USD","x-skills-required":["Product Management","ML/AI Infrastructure","RAG Systems","Vector Databases","Semantic Search","Context Management","Graph Databases","Context Compression","Embedding Models","Retrieval Evaluation Frameworks"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:44:52.232Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Waltham, Massachusetts, United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Product Management, ML/AI Infrastructure, RAG Systems, Vector Databases, Semantic Search, Context Management, Graph Databases, Context Compression, Embedding Models, Retrieval Evaluation Frameworks","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":89200,"maxValue":133800,"unitText":"YEAR"}}}]}