Machine Learning Engineer, Search Quality

Glean Work is hiring a Machine Learning Engineer, Search Quality in Mountain View and San Francisco, United States. It pays $140k-$265k a year and Level rates it ; you can apply on Glean Work.

Level's read on this job

Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include AI agents and NLP.
Level classifies it as a mid-level role.
Level lists 130 other open roles at Glean Work.

AI in this role

ai-agentsnlp

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 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 (4 days a week in our Mountain View or San Francisco 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. 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. #LI-HYBRID 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. Global Data Privacy Notice for Job Candidates and Applicants: Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy. By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement.
By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement, and I agree to the terms.

How we rate this

Machine Learning Engineer, Search Quality at Glean Work rates 83 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Machine Learning Engineer, Search Quality is an open job at Glean Work.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $140k-$265k a year.
The role is based in Mountain View, CA; San Francisco, CA.
The job was posted on April 8, 2022.
Seniority: mid.
Applications are handled by Glean Work. Apply on Glean Work

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsNLP

Also common in machine learning engineer jobs at this level

PyTorch TensorFlow Deep learning AWS Generative AI Kubernetes

Pay

This job starts at: $140k
Typical machine learning engineer job: $199k

This posting starts 30% below the typical machine learning engineer job. See machine learning engineer jobs

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
What NLP problem have you worked on, and how did you measure whether it actually worked?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: AI agents and NLP. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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