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Salary: USD 180,000 - 330,000 / annual
Glean is a Work AI platform that helps enterprises work smarter with AI. The company has evolved from advanced enterprise search into a full-scale Work AI ecosystem, powering intelligent search, AI assistants, and scalable AI agents on a secure, open platform. With over 100 enterprise SaaS connectors and flexible LLM choice, Glean enables organizations to govern, scale, and customize AI across their business without vendor lock-in.
You will join Glean's world-class Data Organization, which comprises product data science, applied science, data engineering, and business intelligence groups. This applied science role focuses on advancing experimentation and evaluation methodologies for AI-driven products.
Key responsibilities include:
- 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.
- Develop and maintain the A/B experimentation platform based on stakeholder feedback.
- Write code or identify vendors to deploy experimentation techniques to production in a scalable, user-friendly manner for engineering, product data science, product management, and design teams.
- Conduct end-to-end evaluation of hero use cases such as document/URL uploads, including evaluation set generation, evaluation criteria definition, and result interpretation.
- 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.
- 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 background in statistics and/or machine learning with demonstrated ability to apply these skills to tangible product improvements, internal tools, and processes in a pragmatic, business-first manner.
- Proficiency in Python at a level sufficient to maintain an internal source-controlled library used by dozens of others.
- Concise and precise written and verbal communication skills; strong technical documentation abilities.
- Proficiency in SQL and the modern data stack (e.g., source-controlled dbt pipelines for ETL/ELT).
- Strong ability to define product KPIs/guardrail metrics, create dashboards, and analyze 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.
- Passion for using AI to improve productivity of data teams and non-data professionals seeking to derive more value from company data.
