Principal UX Researcher, Moveworks

ServiceNow•Santa Clara, CA

25d

About The Position

Moveworks is seeking a mixed-methods Principal Experience Researcher, AI, to turn complex user behavior into actionable product direction. The role combines qualitative research with quantitative analysis to understand what users do and why, and connects end-user experiences with the machine learning systems that support them. The researcher translates user needs, behaviors, and feedback into measurable signals to help teams evaluate and improve model performance and create useful, trustworthy, and intuitive AI experiences. This is a generalist research role spanning methods and stages of product development; an engineering background is not required, but curiosity about AI systems and effective collaboration with technical partners are important. The researcher will shape product strategy and roadmap priorities, set research strategy and portfolio priorities, and establish evaluation approaches and shared quality measures for AI-powered experiences. They will investigate product and model failures, partner with leaders across product, design, engineering, data science, and machine learning, evaluate concepts from discovery through continuous improvement, and communicate findings to technical and nontechnical audiences. The position offers a base pay range of $221,200–$387,100, plus equity when applicable, variable or incentive compensation, and benefits. Compensation varies with qualifications, skill level, competencies, and work location. Benefits include health plans, flexible spending accounts, a 401(k) plan with company match, ESPP, matching donations, a flexible time-away plan, and family leave programs. Work personas may be flexible, remote, or required in office, depending on the nature of the work and assigned work location; eligibility is determined by ServiceNow.

Requirements

Experience leveraging or critically considering how to integrate AI into work processes, decision-making, or problem-solving.
At least 10 years of professional experience in user experience research, product research, human-computer interaction, behavioral science, or a related field.
Strong grounding in qualitative and quantitative research methods, including generative and evaluative approaches, and sound judgment in sampling, survey design, experimentation, bias, validity, and data interpretation.
Demonstrated ability to analyze large or complex behavioral datasets, including product usage logs, and translate findings into actionable product insights.
Strong written, verbal, and visual communication skills, including the ability to make complex findings clear to different audiences.
Strong collaboration and stakeholder-management skills, with experience building alignment across product managers, engineers, and designers.
A portfolio or work samples demonstrating research process, analytical capabilities, and influence on product decisions and user outcomes.

Nice To Haves

Experience conducting lean UX research in a fast-paced environment.
Experience researching AI-powered, machine learning, conversational, search, or recommendation experiences.
Experience developing or improving evaluation systems, benchmarks, rubrics, or quality metrics for AI products.
Familiarity with product analytics, experimentation platforms, data visualization tools, or statistical analysis software.
Experience using SQL, R, Python, or similar tools; hands-on programming is not required.

Responsibilities

Identify and prioritize opportunities across the end-user journey by connecting large-scale behavioral data with qualitative insights, shaping product strategy and roadmap priorities.
Set research strategy and prioritize the research portfolio around high-impact user and business questions, choosing approaches that balance scope, depth, speed, and confidence to guide investment decisions.
Translate user needs and behaviors into clear signals that inform the design, development, and evaluation of AI and machine learning systems.
Define evaluation strategies for AI-powered experiences, including frameworks, quality standards, and shared success measures across product, design, and machine learning teams that connect model performance to usability, relevance, trust, and user outcomes.
Investigate model and experience failures, identify recurring patterns, and recommend improvements across the product and underlying systems.
Partner strategically with product, design, engineering, data science, and machine learning leaders to frame critical questions, challenge assumptions, and align teams on evidence-based product direction.
Evaluate new concepts and product experiences from early discovery and prototyping through launch and continuous improvement.
Communicate findings through clear narratives, data visualizations, research artifacts, and recommendations tailored to technical and nontechnical audiences.