Job description

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: The bottleneck in building reliable and useful AI agents isn't writing code anymore. It's knowing what good looks like, measuring it, and steadily closing the gap. Glean is building the next generation of enterprise AI assistants and autonomous agents that reason, plan, and act across complex, multi-step workflows. This role works at the intersection of applied research, quality, and product engineering to make those systems better. The focus is on quality improvements across Glean's models and agent harnesses. That means hill-climbing with evals, evolving evalsets and agentic verifiers, post-training models for use cases like model routing, and training task-specific models for enterprise workflows. It also means bringing emerging research into the product and working closely with product teams and customers, turning learnings from evals and production usage into better outcomes through quality improvements. You will: Use eval results and production traces to improve the entire stack, including the agent harness, model routing, prompts, and retrieval, while weighing quality against latency and cost. Work on post-training models with supervised fine-tuning (SFT) and preference- or reward-based optimization (e.g., DPO, PPO, GRPO), including generating the data for training. Build RL environments for simulating enterprise workflows, including relevant evalsets, harness support, and LLM-based verifiers for hill-climbing and for internal and external benchmarking. Improve the quality of representative evalsets, LLM judges and agentic verifiers, and calibrate them against human graders so the scores can be trusted in release decisions and model training. Work with product, platform, and research teams across Glean to land quality improvements in production. About you: 5+ years of industry experience in AI or Machine Learning Engineering. Hands-on experience post-training LLMs including adapting open-weight models to specialized tasks. Experience building evals for AI assistant and agent systems, including generating evalsets, LLM-as-a-judge verifiers, calibrating offline and online signals coupled with good judgment about when a metric reflects quality improvements. Strong coding skills in Python and/or Go (TypeScript is a plus), proficiency in your ML framework of choice, and a track record of shipping production ML systems. Product-minded, able to turn a vague problem into a concrete experiment, and thrive in a tight-knit, cross-functional team. Location: This role is hybrid (4 days a week in our Mountain View or San Francisco 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. #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.

Requirements

Must-haves

Use eval results and production traces to improve the entire stack, including the agent harness, model routing, prompts, and retrieval, while weighing quality against latency and cost.
Work on post-training models with supervised fine-tuning (SFT) and preference- or reward-based optimization (e.g., DPO, PPO, GRPO), including generating the data for training.
Build RL environments for simulating enterprise workflows, including relevant evalsets, harness support, and LLM-based verifiers for hill-climbing and for internal and external benchmarking.
Improve the quality of representative evalsets, LLM judges and agentic verifiers, and calibrate them against human graders so the scores can be trusted in release decisions and model training.
Work with product, platform, and research teams across Glean to land quality improvements in production.
5+ years of industry experience in AI or Machine Learning Engineering.
Hands-on experience post-training LLMs including adapting open-weight models to specialized tasks.
Product-minded, able to turn a vague problem into a concrete experiment, and thrive in a tight-knit, cross-functional team.
This role is hybrid (4 days a week in our Mountain View or San Francisco office)
Global Data Privacy Notice for Job Candidates and Applicants:
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.

Nice-to-haves

Strong coding skills in Python and/or Go (TypeScript is a plus), proficiency in your ML framework of choice, and a track record of shipping production ML systems.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmachine learningpythongotypescriptml

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Pto

Wellness