Staff UX Researcher, Agentic AI, Ad Sales and Marketing Platform
Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $188000 - $274000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
- Act as the bridge between user needs and business reality by identifying and clarifying leadership trade-offs to ensure product decisions are both user-centered and commercially viable.
- Transform technical or ambiguous AI issues into actionable research questions and design recommendations.
- Drive AI evaluation for Agentic AI systems.
- Push the boundaries of what the team is currently considering for Agentic AI by advising Director-level and VP-level stakeholders and clarifying the highest-priority problems to solve.
- Design and execute research to continuously ensure that complex technical solutions solve real-world human problems.
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience in product research in an applied research setting,.
- 5 years of experience in statistics, survey research, and the principles of experimental design.
Preferred qualifications:
- Master's degree or PhD in Human-Computer Interaction, Cognitive Science, Statistics, Psychology, Anthropology, or related fields.
- 7 years of experience managing projects, and working in a large organization.
- Expertise in multi-variable experimental methods, including factorial designs, within-subjects/repeated measures, and counterbalancing strategies (e.g., controlling for order and framing effects across multi-agent turns).
- Advanced proficiency in linear mixed-effects models (LMMs/GLMMs) in R or Python, as well as psychometric and comparative scaling methods (e.g., IRT, Bradley-Terry, and Thurstonian modeling) for task-difficulty and rater-bias calibration.