Machine learning Engineer - Agentic Retrieval
What you can expect
Zoom is looking for a Machine Learning Engineer to join our Agentic Retrieval team. You will design and build the core retrieval and reasoning systems that power Zoom's AI Companion - enabling AI agents to search, reason over, and act on enterprise knowledge to deliver high- quality, trustworthy, and actionable answers at scale.
About the Team
The Agentic Retrieval team sits within Zoom's GenAI Engineering organization and is responsible for building a multi-tenant, permission-aware retrieval platform. We operate at the intersection of distributed systems, machine learning, and large language models - powering search and answer generation across meetings, chat, docs, and third-party enterprise applications through a layered API architecture (keyword search, natural language search, and agentic RAG-based answer generation).
Responsibilities:
- Designing and implementing scalable retrieval systems including vector search, hybrid search (keyword + embedding + reranking), and structured query planning.
- Designing and optimize Retrieval-Augmented Generation (RAG) pipelines for multi-step, tool-using AI agents.
- Developing ranking, relevance modeling, and evaluation frameworks to improve search quality and answer grounding.
- Building indexing pipelines that transform heterogeneous enterprise data into unified, retrieval-ready representation.
- Building entity extraction and NLP pipelines that support agentic reasoning over enterprise content.
- Partnering with product, infrastructure, and applied research teams to ship production- grade AI capabilities.
What we're looking for
- Master's degree or higher in Computer Science, Artificial Intelligence, Machine Learning, Distributed Systems, or a related field.
- 5+ years of experience in machine learning, search infrastructure, information retrieval, or distributed systems
- Strong hands-on experience building and operating large-scale search or data platforms in production environments.
- 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++.
- Solid understanding of information retrieval fundamentals (inverted index, ranking,
embeddings, hybrid retrieval).
- Strong system design skills with the ability to reason about scale, reliability, latency, and cost trade-off
Salary Range or On Target Earnings
Not included in the source posting: about the role, what you'll do, benefits.
Skills
Who can apply
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