Senior Quantitative UX Researcher, AI Enablement

Google•Sunnyvale, CA

1d•$159,000 - $230,000

About The Position

At Google, the Senior Quantitative UX Researcher helps create user-focused products by investigating behavior with empirical methods such as logs analysis, survey research, and regression. The role combines behavioral research design, statistical proficiency, and programming skills, and works within a multidisciplinary team alongside Engineering and Product Management. The Quant UXR community offers mentorship, regular meetups, and internal tools. In the AI Enablement side of the ACE UX research team, this researcher drives product excellence for Alphabet’s core AI infrastructure by translating system telemetry into actionable user insights. The role executes partnerships with AI Data and Research-to-Production (R2P), develops measurement frameworks, and uses advanced statistical models and agentic AI workflows to analyze system logs at scale. It partners with Engineering, Product Management, and agentic developers to measure and optimize developer workflows across the AI pipeline.

Requirements

Bachelor's degree or equivalent practical experience.
6 years of experience in product research in an applied research setting.
Experience in quantitative research, logs analysis, measurement and attribution, data analysis, and metrics analysis.
Experience researching AI/ML products, Developer APIs, or technical infrastructure.
Experience with programming languages used for data manipulation and computational statistics; Python is preferred.

Nice To Haves

Master's degree or PhD in Human-Computer Interaction, Cognitive Science, Statistics, Psychology, Anthropology, or a related field.
5 years of experience conducting UX research on products and working with executive leadership, such as Director level and above.
Experience with code reviews.
Experience analyzing behavioral telemetry or log data, such as AI logs or SQL, to inform product decisions.
Experience working with agentic AI tools, Loop Engineering principles, and AI/ML training phases and methods.

Responsibilities

Design and implement agent-assisted pipelines to parse, audit, and analyze large-scale system logs, including telemetry, clickstream, and operational logs.
Define, instrument, and track system-level KPIs, backend performance metrics, and Product Health indicators, aligning them with Critical User Journeys.
Develop custom scripts, prompts, or agentic wrappers, such as in Python or R, to parse complex infrastructure logs and turn them into statistically validated behavioral patterns.
Lead the design and analysis of complex experiments, multivariate testing, and rollout strategies to optimize system configurations, documentation, and tooling.
Collaborate with Infrastructure Engineers, Applied Data Scientists, and Machine Learning researchers to ensure backend pipelines are instrumented for downstream agentic analysis.