Machine Learning Engineer, Search Quality

Glean

San Francisco Bay AreaFull-timeML Engineer15 days ago

About this role

About Glean:

Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles.

At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level.

Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality.

If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company.

About the Role:

Glean is looking for engineers to help build the world’s best search and assistant product for work. Our engineers work on a range of systems across the stack, including query understanding, document understanding, domain-adapted language models, natural language question-answering, evaluation, and experimentation. We interact regularly with customers, deeply understand their pain points, and use whatever tool is necessary, simple or complex, to solve their problems.

You will:

Invent new signals to improve the personalization of our search engine
Train a model to capture interactions between signals in our ranking system
Design smarter ways to domain-adapt language models to each customer’s corpus
Discover new ways of combining LLMs with search engines to answer complex questions
Write robust code that’s easy to read, maintain, and test
Mentor more junior engineers, or learn from battle-tested ones

About you:

2+ years of experience
BA/BS in computer science, math, sciences, or a related degree
Experience working with search, recommendation, natural language processing, or other large systems involving machine learning
Strong analytical skills and ability to work with data
Proven ability to design, build, and ship production-ready models
Proficiency in your ML framework of choice
Strong coding skills (Python, Go, Java, C++, ...)
Thrive in a customer-focused, tight-knit and cross-functional environment - being a team player and willing to take on whatever is most impactful for the company is a must
A proactive and positive attitude to lead, learn, troubleshoot and take ownership of both small tasks and large features

Location:

This role is hybrid (3-4 days a week in one of our SF Bay Area offices)

Compensation & Benefits:

The standard base salary range for this position is $140,000 - $265,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.