AI/ML Intern - Agentic Retrieval & Enterprise RAG
About The Position
About the team You will join the Generative AI team, focused on building intelligent, production-grade AI features used by enterprise customers. The team values curiosity, clear communication, and strong collaboration. You will work closely with engineers and product partners and contribute insights that help shape technical decisions and system design. What you can expect Zoom is building the next generation of enterprise AI systems that power intelligent search, retrieval, and agentic workflows across Zoom products. As an AI Engineer / Machine Learning Engineer Intern, you will work on agentic retrieval and Enterprise RAG systems that operate at production scale and directly impact real users. This role sits at the intersection of large language models, retrieval systems, and production engineering. You will help design, implement, and optimize agentic pipelines that retrieve, reason over, and act on enterprise knowledge with high accuracy, low latency, and strong reliability guarantees. You will contribute to one or more of the following areas: Agentic Retrieval and RAG Systems Design and implement agentic retrieval pipelines that orchestrate multi-step reasoning, retrieval, and tool or function calls Improve end-to-end performance of Enterprise RAG systems, including latency, throughput, and cost efficiency Integrate agentic workflows with unified retrieval layers and enterprise data sources Retrieval Quality and Efficiency Optimize retrieval strategies to reduce redundant calls and unnecessary context expansion Implement content compression or filtering techniques to improve reasoning efficiency while preserving answer quality Analyze retrieval and function-call traces to identify inefficiencies and failure patterns Production-Grade Engineering Build scalable, maintainable ML and retrieval components suitable for production environments Add instrumentation, logging, and evaluation hooks to measure system behavior and product impact Collaborate with engineers and researchers to transition prototypes into reliable services
Requirements
- Be currently pursuing a BS, MS, or PhD in Computer Science, Machine Learning, or a related field
- Have programming skills in Python and familiarity with production ML or backend systems
- Have a solid understanding of information retrieval, NLP, or machine learning fundamentals
- Have experience or coursework related to LLMs, retrieval-augmented generation, or agent-based systems
- Have the ability to reason about system trade-offs such as accuracy, latency, and cost
Nice To Haves
- Have hands-on experience with LLM APIs, prompt engineering, or fine-tuning
- Have familiarity with search systems, vector databases, or ranking pipelines
- Have exposure to agentic workflows, tool or function calling, or multi-step reasoning systems
- Have experience working with real datasets and evaluating model or system performance
- Have an interest in building AI systems with direct product impact, not just research prototypes
Responsibilities
- Design and implement agentic retrieval pipelines that orchestrate multi-step reasoning, retrieval, and tool or function calls
- Improve end-to-end performance of Enterprise RAG systems, including latency, throughput, and cost efficiency
- Integrate agentic workflows with unified retrieval layers and enterprise data sources
- Optimize retrieval strategies to reduce redundant calls and unnecessary context expansion
- Implement content compression or filtering techniques to improve reasoning efficiency while preserving answer quality
- Analyze retrieval and function-call traces to identify inefficiencies and failure patterns
- Build scalable, maintainable ML and retrieval components suitable for production environments
- Add instrumentation, logging, and evaluation hooks to measure system behavior and product impact
- Collaborate with engineers and researchers to transition prototypes into reliable services
Benefits
- Hands-on experience building production-facing AI systems used at enterprise scale
- Exposure to state-of-the-art agentic retrieval and Enterprise RAG architectures
- Mentorship from experienced AI engineers and ML practitioners
- Opportunity to see your work influence real Zoom products and user experiences
- A strong foundation for a career in applied AI, ML engineering, or AI platform development
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What This Job Offers
Career Level
Intern
