Enterprise Graph Retrieval Intern

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.

Responsibilities:

Depending on project needs and individual strengths, you may work in one or more of the following areas:

Design and model enterprise graph schemas

• Design flexible graph schemas representing enterprise relationships, including:

  • Document-to-document relationships such as references, versions, and derivatives

  • Meeting-to-content relationships such as notes, presentations, and follow-ups

  • User-to-content relationships such as authorship, editing, and viewership

  • Topic-based relationships across enterprise content

• Produce clear schema documentation and entity relationship models

Build graph construction pipelines

• Build data processing workflows to extract relationship signals from raw enterprise content

• Apply LLM-based extraction techniques combined with enterprise knowledge bases for entity resolution

• Transform extracted signals into graph edges and properties

• Load, update, and maintain graph data in a graph database such as Amazon Neptune

• Optionally ensure consistency with enterprise access control and permission models

Implement graph-aware querying and retrieval

• Implement graph-aware retrieval mechanisms

• Translate natural language queries into graph queries

• Apply LLM-based query splitting and triplet extraction

• Perform schema-driven graph traversal, subgraph extraction, and multi-hop retrieval

• Support contextual and relationship-based retrieval use cases

Integrate and evaluate retrieval systems

• Integrate graph-based retrieval signals into a unified enterprise retrieval pipeline

• Design evaluation metrics to measure retrieval quality improvements

• Analyze relevance, latency, and scalability trade-offs

• Build end-to-end demos showcasing graph-enhanced search and agentic reasoning

What we're looking for:

(Required)

• 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

Preferred skills

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

Salary Range or On Target Earnings:

Minimum:

Maximum:

In addition to the base salary and/or OTE listed Zoom has a Total Direct Compensation philosophy that takes into consideration; base salary, bonus and equity value.

Note: Starting pay will be based on a number of factors and commensurate with qualifications & experience.

We also have a location based compensation structure;  there may be a different range for candidates in this and other locations.

Ways of Working
Our structured hybrid approach is centered around our offices and remote work environments. The work style of each role, Hybrid, Remote, or In-Person is indicated in the job description/posting.

Benefits
As part of our award-winning workplace culture and commitment to delivering happiness, our benefits program offers a variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health; support work-life balance; and contribute to their community in meaningful ways. Click Learn for more information.

About Us
Zoomies help people stay connected so they can get more done together. We set out to build the best collaboration platform for the enterprise, and today help people communicate better with products like Zoom Contact Center, Zoom Phone, Zoom Events, Zoom Apps, Zoom Rooms, and Zoom Webinars.
We’re problem-solvers, working at a fast pace to design solutions with our customers and users in mind. Find room to grow with opportunities to stretch your skills and advance your career in a collaborative, growth-focused environment.

Our Commitment​

At Zoom, we believe great work happens when people feel supported and empowered. We’re committed to fair hiring practices that ensure every candidate is evaluated based on skills, experience, and potential. If you require an accommodation during the hiring process, let us know—we’re here to support you at every step.

We welcome people of different backgrounds, experiences, abilities and perspectives including qualified applicants with arrest and conviction records and any qualified applicants requiring reasonable accommodations in accordance with the law.

If you need assistance navigating the interview process due to a medical disability, please submit an Accommodations Request Form and someone from our team will reach out soon. This form is solely for applicants who require an accommodation due to a qualifying medical disability. Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed.

Think of this opportunity as a marathon, not a sprint! We're building a strong team at Zoom, and we're looking for talented individuals to join us for the long haul. No need to rush your application – take your time to ensure it's a good fit for your career goals. We continuously review applications, so submit yours whenever you're ready to take the next step.