Sr. Machine Learning Engineer - AI

Zoom • Seattle, WA

23h • Remote

About The Position

We are seeking a highly skilled and motivated Machine Learning Engineer to join our team and play a key role in developing and maintaining our knowledge graph service. As a Machine Learning Engineer specializing in knowledge graphs, you will work closely with cross-functional teams to design, implement, and optimize algorithms and models that enable efficient representation, integration, and retrieval of structured and unstructured data.

Requirements

• Bachelor’s degree in computer science, Communications Engineering, a related field, or a foreign degree equivalent
• 3 years of experience in job offered or related occupation
• 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 stores
• 3 years of experience implementing advanced authentication and security mechanisms for Machine Learning pipelines, including asymmetric JWT and secure key management (AWS KMS, internal CSMS)

Responsibilities

• 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

Benefits

• award-winning workplace culture
• commitment to delivering happiness
• variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health
• support work-life balance
• contribute to their community in meaningful ways
• opportunities to stretch your skills and advance your career in a collaborative, growth-focused environment
• fair hiring practices that ensure every candidate is evaluated based on skills, experience, and potential

What This Job Offers

• Job Type: Full-time
• Career Level: Senior