Machine Learning Engineer, Assistant Quality

Glean Work is hiring a Machine Learning Engineer, Assistant Quality in San Francisco, United States. It pays $180k-$205k a year and Level rates it ; you can apply on Glean Work.

Level's read on this job

On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
The posting allows some remote work.
Tools and skills the posting cites include Python, LLMs and RAG.
Level classifies it as a mid-level role.
Level lists 130 other open roles at Glean Work.

AI in this role

Build and improve the quality of AI assistants and autonomous agents at Glean.

pythonllms

ragai-agentsnlpmachine-learningproduct-engineeringai-assistantsagents

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 Machine Learning Engineer to improve the quality of our AI Assistant and autonomous agents. This role sits at the intersection of production machine learning, LLM-powered systems, and product engineering, with a focus on building, evaluating, and iterating on assistant experiences that are useful, reliable, and grounded in real enterprise workflows.

You will work on applied problems across agent quality, evaluation, personalization, retrieval, and orchestration. The ideal person is excited by shipping production systems, not pure research, and wants to help shape how Glean’s assistant gets better over time through stronger signals, tighter feedback loops, and better end-to-end execution quality.

You will:

Build and improve ML and LLM-powered systems that raise the quality of Glean’s AI Assistant and autonomous agents across real user workflows.
Design evaluation, benchmarking, and monitoring loops to measure assistant quality, model quality, and end-to-end system performance.
Develop and iterate on signals, prompts, workflows, and model-driven logic that improve reasoning, planning, personalization, and task completion quality.
Work across areas such as RAG, semantic search, recommendation-style systems, post-training or reinforcement learning, and agent orchestration where they materially improve product outcomes.
Partner closely with product, design, and engineering teammates to understand customer pain points and ship high-quality production systems quickly.
Contribute to the data and ML infrastructure needed to support robust experimentation, offline and online evaluation, and continuous model improvement.

About you:

2+ years of industry experience in machine learning, applied AI, or software engineering with significant ML ownership.
Strong hands-on coding ability and a track record of shipping production systems, not just prototypes or research projects.
Experience in one or more of the following areas: LLM applications, NLP, search, retrieval, recommendations, evaluation frameworks, agent systems, or personalization.
Comfort working across both modeling and product engineering details, including experimentation, quality measurement, and production iteration.
Proficiency in common ML tooling and strong software engineering fundamentals in languages such as Python, Go, Java, or C++.
A pragmatic, product-minded approach. You know when to use sophisticated ML techniques and when simple, reliable systems are the better answer.
A proactive, low-ego working style and excitement about learning quickly in a high-velocity environment.

Location:

This role is hybrid (4 days a week in our San Francisco office)

Compensation & Benefits: The standard base salary range for this position is $180,000 - $205,000 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, Assistant Quality at Glean Work rates 90 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, Assistant Quality is an open job at Glean Work.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $180k-$205k a year.
The role is based in San Francisco, CA and is marked remote.
The job was posted on August 3, 2026.
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

RAGAI agentsNLPMachine learningProduct EngineeringAI AssistantsAgentsPython

Also common in machine learning engineer jobs at this level

PyTorch TensorFlow Deep learning AWS Generative AI Kubernetes

Pay

This job starts at: $180k
Typical machine learning engineer job: $199k

This posting starts 10% below the typical machine learning engineer job. See machine learning engineer jobs

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
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?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where product engineering was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: RAG, AI agents, NLP, Machine learning, and Product Engineering. 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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