EMPLOYMENT
Full-time
COMPENSATION
$180k - 330k/yr
ARRANGEMENT
Hybrid
At a glance
Lead applied science for Glean’s AI products by building experimentation and evaluation systems, measuring LLM capabilities, and turning rigorous statistical analysis into scalable product improvements.
Summarized by AI from the original posting
What you'll do
- Collaborate with product data science and engineering teams on online A/B experimentation
- Develop and maintain the A/B experimentation platform
- Deploy scalable experimentation techniques to production
- Conduct end-to-end evaluations of AI use cases
- Evaluate tasks including summarization, generation, reasoning, tool use, coding, and system design
- Develop evaluation best practices, tools, and processes
- Define product KPIs and guardrail metrics
- Analyze data to derive strategic insights
Requirements
- 01Strong statistics and/or machine learning background
- 02Very proficient in Python
- 03Proficient in SQL and modern data stack
- 04Experience with ranking, A/B experimentation platforms, or ML measurement
- 05Experience in B2B SaaS
- 06Strong written and verbal communication
Perks
Medical, vision, and dental coverageGenerous time-off policy401(k) planHome office improvement stipendAnnual education stipendAnnual wellness stipendRegular company eventsHealthy lunches daily
Skills
PythonSQLStatisticsMachine LearningA/b ExperimentationLlm EvaluationEvaluation Set GenerationRankingQuery Intent ClassificationProduct Kpi DefinitionGuardrail MetricsDashboarding
Full 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 - 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 building a world-class Data Organization composed of product data science, applied science, data engineering and business intelligence groups. This is an applied science role based in San Francisco office.
You will:
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.
- Collaborate with product data science and engineering teams to identify techniques, tooling and process improvements in online A/B experimentation to assist rigorous decision making across all relevant product domains.
- Develop and maintain our A/B experimentation platform based on stakeholder feedback
- Write code or identify vendors to deploy these techniques to production in a scalable manner that’s easy to use by engineering, product data science, product management and design teams
- Conduct end to end evaluation of various hero use cases like document/URL uploads, including evaluation set generation, coming up with evaluation criteria and methods to interpret the results.
- Break down end to end evaluations into more granular evaluation of various tasks & skills including but not limited to content summarization/analysis/generation, multi-step reasoning & strategizing, tool selection & use, coding & system design.
- Design, develop and own best practices, tools and processes across various evaluation problems, e.g. query intent classification, standardizing the use of best statistical principles to handle LLM stochasticity, industry benchmarking.
- You have 5+ years of experience as a Masters degree holder, 3+ as a PhD degree holders (Masters/PhD degree in Statistics, Mathematics or Computer Science, or another quantitative field)
- You’re strong in statistics and/or machine learning. You have experience in applying these skills into tangible improvements in products, internal tools, and processes in a pragmatic way that puts business urgencies first.
- You are very proficient in Python, e.g. proficient enough to maintain an internal source-controlled library used by dozens of others.
- You are concise and precise in written and verbal communication. Technical documentation is your strong suit.
- You are proficient in SQL and the modern data stack (e.g. source-controlled dbt pipelines for ETL/ELT).
- You are strong at defining good product KPIs/guardrail metrics, dashboarding and analysis of raw data to derive strategic insights.
- You have experience in B2B SaaS.
- You have experience working on ranking, developing, and maintaining A/B experimentation platforms and/or ML measurement problems.
- You are passionate about using AI to improve the productivity of data teams as well as non-data professionals trying to derive more value of their company’s data.
- This role is hybrid (4 days a week in our San Francisco office)
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.