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, useful AI is no longer writing code. It's defining what good looks like and measuring how well our products deliver it. That takes high-quality data and evals that teams can trust. Our team owns data and evaluation science for Glean's AI products. We build evalsets, agentic LLM-judges, and benchmarks that capture what good looks like, along with the platform and infrastructure that run evals reliably at scale, and enterprise simulation environments where we test how our assistants and agents actually perform. Starting from real production usage, you'll turn what we learn into evals that guide improvements across the entire stack, from agent harnesses to tool use, skill iteration, model routing, and post-training of task specific models. You'll bring new research into production and partner with R&D, product, and GTM to turn findings from evals into better products and customer outcomes. If you care about quality and want to build the systems that measure it, this role is for you. You will: Build evalsets and benchmarks from expert-curated examples, production data, synthetic data, and past failures, covering edge cases and key user slices. Build and scale the eval platform and agent observability: experiment pipelines, trace capture and replay, and dashboards, with reproducible, affordable runs. Build agentic LLM verifiers for correctness, groundedness, citation quality, and task success, and calibrate them against human labels using clear rubrics. Run evals at every stage, from CI and pre-release suites to sampled production traffic, and report results with confidence intervals. Build enterprise simulation environments with simulated users and realistic company data to test multi-step agent tasks. Diagnose failures by slice and trace, and turn eval results into concrete fixes, weighing quality against latency and cost. About you: 5+ years of software engineering experience with strong Go and/or Python and backend fundamentals: APIs, data modeling, testing, and distributed pipelines. TypeScript is a plus. Experience building datasets and benchmarks for LLM use-cases, RAG, or agents, including evalset curation and synthetic data generation. Familiarity with eval methodologies and their tradeoffs: deterministic metrics, LLM-as-a-judge, pointwise vs pairwise grading, human labeling, and judge calibration. Experience building eval or experiment infrastructure that runs reliably at scale, including CI/CD integrations and observability tooling like OpenTelemetry. Strong analytical skills: you dig into traces and failure cases, write SQL, and use data and metrics to explain why quality moved. Nice to have: experience with simulation environments and/or model post-training techniques. 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

The bottleneck in building reliable, useful AI is no longer writing code. It's defining what good looks like and measuring how well our products deliver it. That takes high-quality data and evals that teams can trust.
Build evalsets and benchmarks from expert-curated examples, production data, synthetic data, and past failures, covering edge cases and key user slices.
Build and scale the eval platform and agent observability: experiment pipelines, trace capture and replay, and dashboards, with reproducible, affordable runs.
Build agentic LLM verifiers for correctness, groundedness, citation quality, and task success, and calibrate them against human labels using clear rubrics.
Run evals at every stage, from CI and pre-release suites to sampled production traffic, and report results with confidence intervals.
Build enterprise simulation environments with simulated users and realistic company data to test multi-step agent tasks.
Diagnose failures by slice and trace, and turn eval results into concrete fixes, weighing quality against latency and cost.
Experience building datasets and benchmarks for LLM use-cases, RAG, or agents, including evalset curation and synthetic data generation.
Familiarity with eval methodologies and their tradeoffs: deterministic metrics, LLM-as-a-judge, pointwise vs pairwise grading, human labeling, and judge calibration.
Experience building eval or experiment infrastructure that runs reliably at scale, including CI/CD integrations and observability tooling like OpenTelemetry.
Strong analytical skills: you dig into traces and failure cases, write SQL, and use data and metrics to explain why quality moved.
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

5+ years of software engineering experience with strong Go and/or Python and backend fundamentals: APIs, data modeling, testing, and distributed pipelines. TypeScript is a plus.
Nice to have: experience with simulation environments and/or model post-training techniques.

Application prep

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

Keywords to mirror

aillmgopythontypescriptci/cdsql

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Pto

Wellness