Enterprise Graph Retrieval Intern
About The Position
About the team The team builds Agentic Retrieval systems that power intelligent search, retrieval, and reasoning across Zoom’s enterprise platforms. We work at the intersection of knowledge graphs, retrieval systems, large language models, and production engineering to enable scalable, reliable access to enterprise knowledge. Interns are treated as full contributors and work on real problems alongside experienced engineers and scientists. What you can expect As a Machine Learning, Applied Scientist, or Research Engineer Intern, you will contribute to Enterprise Graph Retrieval within the Agentic Retrieval systems that support Zoom . You will work on building graph-based retrieval and reasoning capabilities that enhance enterprise RAG systems and enable intelligent AI agents. Your work will focus on designing, building, and evaluating graph-powered retrieval systems that operate on real enterprise data and directly impact users across Zoom products. You will collaborate closely with engineers and product partners, contribute to technical discussions, and deliver working components, evaluations, and demos that showcase graph-enhanced search and agentic reasoning.
Requirements
- Currently pursuing a BS, MS, or PhD in Computer Science, Machine Learning, AI, or a related field
- Demonstrate strong programming skills in Python; Java is a plus
- Apply solid foundations in algorithms, data structures, and system design
- Show interest in information retrieval, RAG systems, or knowledge-centric AI
Nice To Haves
- Graph & Knowledge Representation Experience with knowledge graphs , property graphs , or RDF-based systems
- Familiarity with graph query languages (Gremlin, SPARQL, Cypher, or similar)
- Understanding of graph modeling , schema design, and relationship semantics
- Experience with graph databases (e.g., Amazon Neptune, Neo4j, JanusGraph)
- GraphRAG & Retrieval Hands-on experience with GraphRAG or hybrid graph + vector retrieval systems
- Knowledge of combining symbolic graph reasoning with neural retrieval
- Experience integrating graph signals into ranking or retrieval pipelines
- Familiarity with subgraph extraction, path-based reasoning, or multi-hop retrieval
- Applied ML / Research Experience using LLMs for:
- Information extraction
- Entity resolution / linking
- Query understanding or rewriting
- Exposure to retrieval evaluation metrics (e.g., relevance, recall, task success)
- Ability to design experiments and analyze results for retrieval quality improvements
- Systems & Scaling Experience building data pipelines for large-scale datasets
- Familiarity with cloud-native systems (AWS preferred)
- Understanding of performance, latency, and scalability trade-offs in retrieval systems
Responsibilities
- Design and model enterprise graph schemas
- Build graph construction pipelines
- Implement graph-aware querying and retrieval
- Integrate and evaluate retrieval systems
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What This Job Offers
Career Level
Intern
