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# AI Software Engineer - Search Infrastructure

- **Company:** Zoom Video Communications, Inc.
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $151,800.0 - $332,200.0
- **Contract:** Permanent contract
- **Skills:** Microsoft Windows, Artificial Intelligence, Amazon Web Services, Big Data, Software as a Service, Data Retrieval, Data Security, Distributed Systems, Elasticsearch, Monitoring of Systems, Queueing Systems, Systems Integration, Data Ingestion, Large Language Models, Backend, Kubernetes, Slack, Low Latency, Performance Monitor, Gsuite, Api Design, Docker
- **Published:** June 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=fe9fd40564dcde13

## About the Role

Do you have experience in System performance monitoring?, Do you have a Bachelor's degree?, * Have a Bachelor's degree and 4+ years of experience in backend or distributed systems engineering

 * Have a productivity mindset with experience using AI tools effectively
 * Have experience designing and operating large-scale data ingestion pipelines (message queues, vector stores, Temporal, Elasticsearch etc.)
 * Demonstrate a track record of building highly available, multi-tenant backend services
 * Have experience with document-level permission modeling and secure data handling
 * Possess with cloud-native tools such as Docker, Kubernetes, and AWS
 * Have experience in Go is a bonus
 * Have experience integrating with SaaS platforms (Google Workspace, Microsoft 365, Slack, etc.)

## Description

You'll collaborate with world-class engineers, designers, and product thinkers to define what "AI-powered search" really means in the enterprise. As a core engineer on this team, you'll work across real-time document pipelines, vector databases, and permission-aware retrieval to push the boundaries of applied LLM systems at scale., * Designing and implementing a scalable RAG system for real-time Q&A across internal content (meetings, messages, documents, whiteboards, videos, etc.).

 * Building robust ingestion and indexing pipelines for semi-structured data sources with fine-grained, permission-aware access control.
 * Developing APIs and backend systems to enable efficient querying, retrieval, and ranking.
 * Collaborating with ML/NLP engineers to iterate on embedding models and improve search quality.
 * Ensuring reliability, low latency, and scalability across the entire data retrieval and augmentation stack.
 * Monitoring system performance and optimize for high-throughput, low-latency workloads under real-world load.


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