Sr. Machine Learning Engineer - AI

Zoom; United States; On-site; USD 135,000 - 180,000; Full time

Job summary

Zoom is seeking an experienced ML Engineer to architect and optimize large-scale ML workflows for knowledge graphs and search services. You will deploy, monitor, and maintain models in a microservice environment using Docker, Kubernetes, and internal platforms, while collaborating with cross-functional teams to meet requirements.

You will document AI search systems and lead the development of AI-driven search pipelines, including document understanding and retrieval, with emphasis on

Qualifications

• Bachelor's degree in computer science or related field.
• Minimum 3 years of relevant experience in ML systems.
• Proven experience designing and deploying document understanding pipelines.
• Experience deploying ML models in microservice environments with Docker/Kubernetes (AWS EKS).
• Ability to evaluate data sources for AI search/document understanding.
• Strong in metrics, monitoring, and performance optimization.

Responsibilities

• Architect and optimize large-scale ML workflows for knowledge graphs and search services.
• Deploy, monitor, and maintain ML models (LLMs) in microservice environments.
• Collaborate with cross-functional teams to define requirements and deliver production ML services.
• Maintain documentation for AI search systems using Confluence, Zoom Docs, and LucidChart.
• Develop routing logic for ML models and manage feature stores.
• Implement authentication and security for ML pipelines (JWT, AWS KMS).
• Lead design and deployment of AI search pipelines with document understanding.
• Develop performance metrics and monitoring for scalable, low-latency AI systems.

Skills

ML pipelines

Distributed systems

Docker/Kubernetes

Knowledge graphs

NLP

Security best practices

API design

Documentation

Mentoring

Education

Bachelor's degree in CS or related field

Tools

Docker

Kubernetes

ZCP

Confluence

Zoom Docs

LucidChart

AsyncMQ

Job description

Immigration sponsorship is not available for this position

Immigration sponsorship is not available for this position

Responsibilities:

• Architect and optimize large-scale Machine Learning workflows to process structured and unstructured data for knowledge graphs and search services;
• Architect and optimize large-scale Machine Learning workflows to process structured and unstructured data for knowledge graphs and search services;
• Deploy, monitor, and maintain machine learning models (LLMs and agentic workflows) in a microservice environment using Docker, Kubernetes (AWS EKS), and internal platforms (ZCP);
• Collaborate with cross-functional teams of engineers, product managers, and domain experts to define requirements and implement scalable, production-ready Machine Learning services.
• Maintain comprehensive documentation for all AI search systems, workflows, and services using Confluence, Zoom Docs, and LucidChart;
• Develop business logic for routing requests to Machine Learning models and managing feature stores;
• Implement advanced authentication and security mechanisms for Machine Learning pipelines, including asymmetric JWT and secure key management (AWS KMS, internal CSMS.
• Work with asynchronous, distributed messaging frameworks (AsyncMQ).
• Optimize knowledge graph algorithms for performance, scalability, and reliability.
• Conduct research on cutting-edge Machine Learning, NLP, and knowledge graph techniques, evaluating external data sources to enhance search and document understanding capabilities;
• Develop/maintain documentation, best practices, guidelines;
• Mentor junior engineers and contribute to team knowledge-sharing, best practices, and internal technical guidelines;
• Lead the design, development, and deployment of AI search pipelines, including document understanding and retrieval systems, using libraries such as Docling;
• Evaluate external data sources to enhance AI search/document understanding;
• Develop performance metrics, monitoring systems, and optimizations for AI search pipelines to ensure scalability, efficiency, and low latency;
• Develop and maintain AI-driven search and ranking algorithms for enterprise-scale information retrieval and RAG (Retrieval‑Augmented Generation) systems;
• Design and maintain data ingestion, preprocessing, and transformation pipelines for both structured and unstructured data, ensuring high data quality and reliability.

What we're looking for:

• Requires a bachelor's degree in computer science, Communications Engineering, a related field, or a foreign degree equivalent.
• Must have 3 years of experience in job offered or related occupation.
• Must have 3 years of experience in the following:
• 3 years of experience in design, development, and deployment of document understanding and processing pipelines;
• 3 years of experience in deploy, monitor, and maintain machine learning models (LLMs and agentic workflows) in a microservice environment using Docker, Kubernetes (AWS EKS), and internal platforms (ZCP);
• 3 years of experience in evaluating external data sources to enhance AI search/document understanding;
• 3 years of experience in developing performance metrics, monitoring systems, and optimizations for AI search pipelines to ensure scalability, efficiency, and low latency;
• 3 years of experience in design and maintaining data ingestion, preprocessing, and transformation pipelines for both structured and unstructured data, ensuring high data quality and reliability;
• 3 years of experience in architect and optimizing large-scale Machine Learning workflows to process structured and unstructured data for knowledge graphs and search services;
• 3 years of experience collaborating with cross-functional teams of engineers, product managers, and domain experts to define requirements and implement scalable, production-ready ML services;
• 3 years of experience optimizing knowledge graph algorithms for performance, scalability, and reliability.
• 3 years of experience conducting research on cutting‑edge Machine Learning, NLP, and knowledge graph techniques, evaluating external data sources to enhance search and document understanding capabilities;
• 3 years of experience developing and maintaining AI-driven search and ranking algorithms for enterprise‑scale information retrieval and RAG (Retrieval‑Augmented Generation) systems;
• 3 years of experience maintaining comprehensive documentation for all AI search systems, workflows, and services using Confluence, Zoom Docs, and LucidChart;
• 3 years of experience developing business logic for routing requests to Machine Learning models and managing feature stor