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Salary: USD 180,000 - 330,000 / annual
Glean is a Work AI platform that helps enterprises work smarter with AI through intelligent search, AI assistants, and scalable AI agents. The company has evolved from advanced enterprise search into a full-scale Work AI ecosystem with 100+ enterprise SaaS connectors, flexible LLM choice, and robust APIs. Recognized by Fast Company (Top 10 Most Innovative Companies 2025), CNBC's Disruptor 50, and Gartner's Tech Innovators in Agentic AI, Glean serves 1,000+ employees across 50+ industries in 25+ countries.
This applied science role is part of Glean's world-class Data Organization, which includes product data science, applied science, data engineering, and business intelligence groups. You will collaborate with product data science and engineering teams to identify techniques, tooling, and process improvements in online A/B experimentation to support rigorous decision-making across product domains. You will develop and maintain the A/B experimentation platform based on stakeholder feedback, write code or identify vendors to deploy techniques to production in a scalable manner accessible to engineering, product data science, product management, and design teams.
Key responsibilities include conducting end-to-end evaluation of hero use cases like document/URL uploads, including evaluation set generation, evaluation criteria development, and result interpretation. You will break down end-to-end evaluations into granular evaluation of various tasks and skills including content summarization/analysis/generation, multi-step reasoning and strategizing, tool selection and use, and coding and system design. You will design, develop, and own best practices, tools, and processes across evaluation problems such as query intent classification, standardizing statistical principles to handle LLM stochasticity, and industry benchmarking.
The role is hybrid, requiring 4 days per week in the San Francisco office.
REQUIREMENTS:
- 5+ years of experience as a Master's degree holder, or 3+ years as a PhD degree holder (Master's/PhD in Statistics, Mathematics, Computer Science, or another quantitative field)
- Strong in statistics and/or machine learning with experience applying these skills to tangible product, tool, and process improvements in a pragmatic, business-first manner
- Very proficient in Python (proficient enough to maintain an internal source-controlled library used by dozens of others)
- Concise and precise in written and verbal communication; strong technical documentation skills
- Proficient in SQL and the modern data stack (e.g., source-controlled dbt pipelines for ETL/ELT)
- Strong at defining good product KPIs/guardrail metrics, dashboarding, and analysis of raw data to derive strategic insights
- Experience in B2B SaaS
- Experience working on ranking, developing, and maintaining A/B experimentation platforms and/or ML measurement problems
- Passionate about using AI to improve productivity of data teams and non-data professionals deriving value from company data
