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Open nowFirst seen 5 hours ago

Staff UX Quantitative Researcher, Agentic AI Ad Sales

Google3,262 open roles

Where
Mountain View, CA, USA
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Your applicationOpen nowStaff UX Quantitative Researcher, Agentic AI Ad SalesGoogle · Mountain View, CA, USA

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The clock on this job

Early applications get read.

8.5% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 27 days.

Share of postings closed within
  1. 1.8%1 day
  2. 4.0%3 days
  3. 8.5%7 days
  4. 15.4%14 days
  5. 34.1%30 days
This job: first seen 5 hours ago

Google median: 27 days open

The posting

Minimum qualifications:

  • Bachelor's degree in Human-Computer Interaction, Cognitive Science, Statistics, Psychology, Anthropology, a related field, or equivalent practical experience.
  • 8 years of experience in an applied research setting, or similar.
  • Experience in programming languages used for data manipulation and computational statistics (e.g., Python, R, MATLAB, C++, Java, or Go).
  • Experience with agentic AI evaluation: evaluating multi-agent architectures, agentic workflows, orchestrators, or subagent routing systems.

Preferred qualifications:

  • 8 years of experience conducting UX research on products.
  • 5 years of experience managing projects, and working in a large, matrixed organization.
  • Experience developing evaluation rubrics for human raters and AI models.
  • Experience with advanced statistical and psychometric modeling.
  • Expertise in multivariate experimental design and methods for causal inference.

About the job

At Google, we "Focus on the user and all else will follow." As a User Experience Researcher (UXR), you make this possible. You will join our multi-disciplinary UX team, collaborating with Engineering and Product Management, to gather insights and create industry-leading products.

You’ll play a critical role in helping your team understand user needs and will drive impact at all stages of product development, ensuring products are useful, usable, and helpful. You will explore user behaviors and motivations by conducting primary research, including: field studies, interviews, diary studies, ethnography, surveys, usability testing, and logs analysis. You will also grow within a supportive UXR community offering mentorship, regular meetups, and exclusive internal tools.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

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.

Responsibilities

  • Apply statistical methods (e.g., statistical inference, regression modeling, experimental design) to analyze large-scale log data, survey data, and other behavioral data.
  • Partner with product managers, engineers, and designers to identify research opportunities, define user-centered success metrics, and ensure appropriate data instrumentation.
  • Translate complex findings into actionable insights and communicate them effectively to cross-functional stakeholders at various levels. Ensure research doesn't just result in a report, but in a shipped product that moves the needle on revenue and seller effectiveness.
  • Collaborate with qualitative UXRs, data scientists, and business analysts to triangulate findings and provide a holistic view of the user experience.
  • Design and analyze human and synthetic evaluation systems, applying crossed rater/item statistical models and bradley-terry preference frameworks to calibrate agent routing quality and mitigate rater bias.
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