Machine Learning Engineer, Enterprise Brain
Where you can work from
🇺🇸 United States
Posting location: Mountain View, CA. Eligibility is read from the employer's own posting and usually requires the right to work in the country shown — confirm on the company's page before applying.
Open to applicants from: United States
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 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. You'll be 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’s Enterprise Brain team builds applied ML systems that help Glean understand a user’s work and anticipate useful next steps, including task prediction and proactive assistance. The work combines LLMs and traditional ML with signals from the Enterprise Graph, ranking and retrieval, and orchestration. You’ll own production ML problems from problem definition and evaluation through launch and monitoring, with a focus on measurable improvements to product quality.
You will:
- Define and solve applied ML problems involving user-work understanding, task prediction, and proactive assistance.
- Combine LLMs and traditional ML with Enterprise Graph signals, ranking/retrieval, and orchestration to improve product quality.
- Build and ship production ML systems, including the evaluation, benchmarking, and data-quality loops needed to measure their impact.
- Own the lifecycle from problem definition and baseline through launch, monitoring, and iteration.
- Partner with product and engineering to understand customer problems and deliver reliable, production-ready solutions
About you:
- You bring 6+ years of industry experience in machine learning engineering.
- You hold a BA/BS in computer science, math, sciences, or a related field.
- You have a proven ability to design, build, and ship production-ready models and systems.
- You bring demonstrated expertise in ML evaluation, benchmarking, and data quality.
- You are proficient in an ML framework of your choice, such as TensorFlow or PyTorch.
- You thrive in a fast-moving, ambiguous environment, turning open-ended customer and ML problems into clear priorities and production-ready solutions.
- You balance speed with quality, making pragmatic tradeoffs to deliver measurable progress without losing sight of production readiness.
- This role is hybrid (4 days a week in our Mountain View office)
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