Staff Machine Learning Engineer

Zoom • Seattle, WA

1d • Hybrid

About The Position

What you can expect Zoom is building the next generation of AI Companion, powered by Agentic Retrieval — a unified intelligence layer that enables AI agents to securely search, reason over, and act on enterprise knowledge at scale. In this role, you’ll design and develop the core retrieval and reasoning systems driving AI-powered workflows across meetings, chat, docs, and enterprise apps. This is a high-impact opportunity to shape Zoom’s AI foundation and advance how enterprises access and use knowledge. About the Team The Agentic Retrieval team within Zoom’s GenAI Engineering group builds a multi-tenant, permission-aware retrieval platform. It powers large-scale document and data search. The team also focuses on enterprise knowledge graph reasoning and retrieval orchestration for AI agents. We operate at the intersection of distributed systems, machine learning, knowledge representation, and large language models. Our mission is to enable AI agents to access the right information, with the right permissions, at the right time — across enterprise data sources.

Requirements

• 5+ years of experience in machine learning, search infrastructure, information retrieval, distributed systems, or related areas.
• Demonstrate hands-on experience building and operating large-scale search, recommendation, or data platforms in production environments.
• Possess understanding of:Information retrieval, Structured query systems, Knowledge graph or graph database concepts
• Have experience building or integrating RAG systems and LLM-based applications in production.
• Possess proficiency in one or more of: Python, Go, Java, C#, or C++.
• Have experience with modern ML frameworks (e.g., PyTorch, TensorFlow) and vector databases or search engines.

Nice To Haves

• Have experience working with enterprise data systems or multi-tenant SaaS environments is a plus.

Responsibilities

• Designing and building scalable retrieval systems using vector and semantic search for unstructured data, hybrid keyword-embedding search, enterprise data analytics, and graph-based reasoning.
• Designing and optimize Retrieval-Augmented Generation (RAG) pipelines for multi-step, tool-using AI agents.
• Building indexing pipelines that transform heterogeneous enterprise data into unified retrieval-ready representations.
• Developing ranking, relevance modeling, and evaluation frameworks to improve answer quality and grounding.
• Working on permission-aware retrieval to ensure secure, multi-tenant isolation and fine-grained access control.
• Partnering closely with product, infrastructure, and applied research teams to ship production-grade AI capabilities.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed