Senior Product Manager - Core AI (Understand)

Qualtrics • Seattle, WA

1d • $166,500 - $218,500 • Hybrid

About The Position

At Qualtrics, we create software the world’s best brands use to deliver exceptional frontline experiences, build high-performing teams, and design products people love. We are more than a platform—we are the creators and stewards of the Experience Management category serving over 18K clients globally. Building a category takes grit, determination, and a disdain for convention—but most of all it requires close-knit, high-functioning teams with an unwavering dedication to serving our customers. When you join one of our teams, you’ll be part of a nimble group that’s empowered to set aggressive goals and move fast to achieve them. Strategic risks are encouraged and complex problems are solved together, by passing the mic and iterating until the best solution comes to light. You won’t have to look to find growth opportunities—ready or not, they’ll find you. From retail to government to healthcare, we’re on a mission to bring humanity, connection, and empathy back to business. Join over 5,000 people across the globe who think that’s work worth doing.

Senior Product Manager, Core AI (Understand)

Why We Have This Role
Define the future of Qualtrics' Understand layer — the intelligence that turns raw experience data into structured meaning, prediction, and insight across the entire portfolio.
Own the product strategy for the capabilities that let AI systems and product teams reason about experience data: ontologies and semantic systems, text analytics enrichments, prediction, simulation, and benchmarking.
Own the Core AI platform foundations that these capabilities depend on: agent infrastructure, context and memory, tools and orchestration, agent evaluation, observability, and AI safety.
Manage the entire lifecycle for multiple functional areas of Understand, from framing the problem, to aligning on architecture and product direction, to forming the plan, delivering implementation, and iterating until the capabilities are world-class.

Requirements

• Bachelor's degree in Engineering, Computer Science, Data Science, Business, or a related field.
• 8+ years of product management experience, including significant experience building technically complex platforms, AI/ML products, data systems, developer platforms, or analytics products.
• A proven track record of defining product strategy and delivering technically sophisticated products in partnership with engineering, machine learning, data science, or AI research teams.
• Hands-on product experience in one or more of the following is strongly preferred: Ontologies, knowledge graphs, semantic layers, taxonomies, or metadata systems.
• Text analytics, NLP, or LLM-based enrichment of unstructured data — sentiment, topics, intent, entities, summarization.
• Predictive modeling, forecasting, driver analysis, or causal inference products.
• Simulation, synthetic data, or scenario modeling.
• Model and system benchmarking, or AI/agent evaluation systems.
• Agentic AI systems and agent orchestration.
• Retrieval, context engineering, or AI memory systems.
• LLM infrastructure, model gateways, inference platforms, or AI developer platforms.
• AI observability, experimentation, safety, governance, or reliability.
• Strong understanding of modern AI system architecture and the tradeoffs involved in building production-grade AI applications.
• Comfort reasoning about model quality: how to measure it, how to improve it, and how to communicate it credibly to customers.
• Ability to operate comfortably at both the strategic and technical levels, from articulating a multi-year vision to working with engineers and researchers on detailed product and architecture decisions.
• Strong understanding of enterprise software requirements, including security, permissions, privacy, governance, reliability, explainability, and scale.
• Excellent analytical and problem-solving skills, particularly in ambiguous technical domains where best practices are still emerging.
• Ability to translate complex technical concepts into clear product strategy and communicate effectively with technical and non-technical audiences.
• Strong communication and collaboration skills, with demonstrated ability to influence senior leaders and build alignment across diverse teams.
• Experience creating products that serve internal developers, external customers, or both is a strong plus.

Nice To Haves

• The Understand team at Qualtrics builds the intelligence layer that turns experience data into meaning — and powers AI experiences across our product portfolio.
• Our mission is to make Qualtrics the best place to build AI experiences grounded in the rich structure, meaning, and context of experience data.
• We believe the next generation of enterprise AI will require more than access to increasingly capable models. It requires infrastructure that gives AI systems the right context, understanding, tools, evaluation, permissions, and feedback loops to reliably accomplish meaningful work — and models that genuinely understand what customers and employees are telling you.
• The team spans ontologies and semantic systems, text analytics enrichments, prediction, simulation, and benchmarking, alongside the agent, orchestration, context and memory, evaluation, observability, model infrastructure, and governance capabilities that support them.
• We are seeking a Principal Product Manager who can define this layer, identify the highest-leverage investments, and partner deeply with engineering and AI leaders to turn rapidly evolving technology into durable product advantage.
• This role will have broad influence across Qualtrics. You will work with teams across our portfolio to understand the AI experiences they want to create, identify common needs, and build reusable capabilities that increase the speed, quality, and ambition of AI development across the company.

Responsibilities

• Partner with product, engineering, data science, research, and design teams across Qualtrics to understand what enrichment, modeling, and platform capabilities they need to build exceptional AI products.
• Develop a deep understanding of the needs of both enterprise customers and internal AI product builders, and translate those needs into strategy, requirements, and roadmaps.
• Define product strategy across the Understand surface area: ontologies and semantic layers, text analytics and enrichment pipelines, predictive models, simulation, benchmarking, and the agent runtime, orchestration, memory, evaluation, and guardrail capabilities that support them.
• Prioritize investments based on customer value, insight quality, developer productivity, technical leverage, reuse across Qualtrics products, and opportunities for competitive differentiation.
• Collaborate deeply with engineering, AI research, and data science teams to make thoughtful product and architectural tradeoffs in a rapidly evolving technical landscape.
• Develop clear frameworks for evaluating the quality, accuracy, reliability, safety, and business impact of enrichment models, predictive systems, and agentic AI.
• Build the benchmarking discipline that lets Qualtrics prove its models and enrichments are better than alternatives — internally and to customers.
• Create shared capabilities that accelerate AI development across Qualtrics while providing the reliability, governance, security, and observability required by enterprise customers.
• Develop and communicate a compelling vision and roadmap to senior leaders, product teams, technical stakeholders, and customers.
• Define and monitor meaningful KPIs for adoption, model and enrichment quality, prediction accuracy, evaluation performance, developer velocity, reliability, and customer impact.
• Stay at the forefront of developments in foundation models, agents, evaluation methods, semantic systems, causal and predictive modeling, simulation, and enterprise AI infrastructure — and translate them into concrete product opportunities.
• Develop and execute the product strategy for Qualtrics' Understand layer.
• Define the foundational architecture and capabilities required for teams across Qualtrics to build reliable, differentiated AI experiences.
• Lead product strategy for areas including: Ontologies and semantic layers — grounding AI systems in the meaning and relationships within enterprise experience data. Text analytics and enrichments — topic and theme detection, sentiment and emotion, intent, effort, entity extraction, summarization, and the enrichment pipelines that make unstructured feedback machine-usable at scale. Prediction — predictive models over experience and operational data, including churn, satisfaction, and outcome modeling, and the drivers behind them. Simulation — modeling how experience programs, interventions, and design choices would perform before they ship, including synthetic respondents and scenario modeling. Benchmarking — internal and external benchmarks for model and enrichment quality, plus customer-facing benchmarks that put results in competitive and industry context. Agent infrastructure — orchestration, planning, tool use, delegation, and multi-agent patterns. Agent and model evaluation — offline and online evaluation systems, task success measurement, quality frameworks, regression testing, and human-in-the-loop evaluation. Context engineering, memory, and retrieval — mechanisms for providing agents with the right information at the right time. AI observability and debugging — capabilities that help teams understand why AI systems behave the way they do. Model infrastructure and abstraction layers — letting Qualtrics teams use the right models for the right tasks while managing cost, latency, reliability, and quality. Guardrails, permissions, governance, and safety — the systems required for trusted enterprise AI.
• Work closely with product teams across Qualtrics to understand their AI use cases and identify opportunities for shared capabilities.
• Connect with enterprise customers to understand their expectations for accurate, explainable, governed, and reliable AI systems.
• Discover and prioritize requirements from product teams, customers, prospects, analysts, researchers, engineers, and the broader AI ecosystem.
• Write Product Investment Documents and turn strategy into clear investment decisions, roadmaps, and measurable outcomes.
• Manage complex cross-functional work across product, engineering, AI research, data science, UX design, and research.
• Create strong adoption strategies so that new capabilities are easy to discover, easy to use, and meaningfully improve the speed and quality of AI product development.
• Know the technical landscape, adoption metrics, model quality measures, and emerging market trends better than anyone.
• Lead the rollout of new capabilities, including internal adoption, developer enablement, documentation, customer communication, and external positioning where appropriate.

Benefits

• Experience Bonus: Qualtrics offers US employees an annual $1,800 “experience bonus” to provide an experience they might not otherwise have—attend a sporting event or concert, travel somewhere new, or even support a nonprofit or infuse funds into a small business in your area.
• Learning and Development: All team members are encouraged to devote 10% of their time to personal learning and development.
• The opportunity to shape foundational technology that powers AI experiences across the Qualtrics portfolio.
• The opportunity to work on some of the most important and rapidly evolving problems in AI product development.
• A role with significant technical depth, strategic influence, and cross-company impact.
• The satisfaction of building platforms that enable teams across Qualtrics to create more ambitious, reliable, and valuable AI products.
• Full-time employees are eligible for medical, dental, vision, life and disability, 401(k) with match, paid time off, a wellness reimbursement, mental health benefits, and an experience bonus.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior