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 an AI Data Analyst to help power the human evaluation system behind Glean’s AI product quality. This role sits at the centre of response quality, competitive benchmarking, and release readiness. You will evaluate AI outputs, label and categorize failure modes, maintain high-quality benchmark data, and help ensure the datasets and evals we rely on are realistic, consistent, and tied to measurable product quality lift. This is a highly cross-functional role. You will work closely with QA, Engineering, Product, and the evals team to convert ambiguous quality issues into structured data, actionable feedback, and repeatable quality signals.
You will
Run ongoing human labeling for priority AI workflows, including response quality, task success, MCP and tool use, end-to-end Cowork-style workflows.
Triage bad-query reports, downvotes, escalations, and routed quality issues; categorize failure modes and feed clean analysis back to partner teams.
Perform qualitative analysis to identify recurring patterns such as hallucinations, retrieval failures, tool-use issues, weak grounding, and poor workflow completion.
Follow and improve labeling guidelines and rubrics so judgments are consistent, realistic, and useful for model and product improvement.
Maintain high-quality labeled datasets, golden sets, and regression slices; monitor coverage, drift, leakage, and difficulty distribution.
Participate in calibration exercises with human graders and validate LLM-as-a-judge outputs against human labels to improve consistency and judge quality.
Partner with engineers, PMs, QA, and eval owners on pre/post-change quality reads, benchmark updates, and release-readiness decisions.
Contribute to recurring quality reporting by surfacing top failure modes, coverage gaps, quality shifts, and actionable recommendations.
About you
3–5 years of experience in data labeling, data analysis, QA, or a related field.
Strong analytical judgment and attention to detail, with the ability to apply nuanced rubrics consistently.
Experience evaluating AI-generated outputs or working with NLP, search, recommendation, or other ML systems.
Familiarity with structured qualitative analysis, basic SQL/data retrieval, and spreadsheet-based workflows.
Clear written and verbal communication skills, with the ability to explain findings to both technical and non-technical audiences.
Ability to work independently in ambiguous, fast-moving environments and collaborate effectively across functions.
Location
This role is hybrid (4 days a week in our offices).
Compensation & Benefits
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 are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on 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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