Machine Learning Engineer, Enterprise Brain
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Summary
Machine Learning Engineer on Glean's Enterprise Brain team building proactive AI products that detect and automate enterprise tasks. Day-to-day involves LLM workflows, reinforcement learning and fine-tuning, ranking, evaluation/benchmarking loops, and ML pipelines on the enterprise knowledge graph. Hybrid role, 4 days/week in Mountain View, CA.
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. 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 is seeking a few Machine Learning engineers who want to focus on a combination of Quality and traditional ML work to help us build the Enterprise Brain. The Enterprise Brain team is developing a suite of proactive AI products that aims to revolutionize enterprise workflows by proactively detecting and automating tasks for users - thus unlocking true productivity. This is built on top of a deep user understanding and state of the art Enterprise graph. The project involves using both LLM and other advanced ML techniques, agent orchestration and cutting-edge ranking techniques.
You will:
- Work on deeply challenging ML problems involving user understanding and task prediction.
- Invent new LLM workflows and signals to improve reasoning, planning, and personalization.
- Design and optimize reinforcement learning and fine-tuning approaches to improve the quality of understanding, prediction and other agentic systems.
- Lead development of scalable evaluation, benchmarking, and optimization loops.
- Build and maintain robust ML pipelines for enterprise and knowledge graph construction.
- Drive initiatives to measure, monitor, and improve data quality, model quality, and end-to-end system performance.
- Collaborate with cross-functional teams to deeply understand customer pain points and deliver high-quality, production-ready ML solutions.
- Mentor junior engineers or learn from experienced ones in a tight-knit, high-velocity environment.
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.
Location:
- This role is hybrid (4 days a week in our Mountain View office)
Compensation & Benefits:
The standard base salary range for this position is $200,000 - $280,000 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.
We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused.
We’re committed to building and sustaining a diverse, inclusive workplace. We strive to attract and retain people with a wide range of backgrounds, experiences, and perspectives, and we do not discriminate on the basis of gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.
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AI-First Mindset at Glean:
At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required.
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Skills
As published by greenhouse · 8 questions · 1 written answer
Basics
First Name, Last Name, Email, Phone, Resume/CV, Cover Letter
Short answers (4)
- LinkedIn Profile
- Do you know anyone currently at Glean?
- Please input the total years of experience you have that are relevant for this role.
- Describe one production ML system relevant to this role that you personally shipped. What did you own, what baseline or metric did you use, what tradeoff or failure did you encounter, and what changed after launch?
Pick from a list (3)
- How did you hear about Glean?
- Are you willing and able to commit to the hybrid policy if hired?
- Do you require sponsorship? optional
Written answers (1)
- What AI tools are you currently using today and how are you using them?