Senior Product Manager - Core AI (Understand)

Qualtrics•Seattle, WA

18h•$166,500 - $218,500•Hybrid

About The Position

Qualtrics is seeking a Senior Product Manager to define the future of its Core AI 'Understand' layer. This role involves owning the product strategy for capabilities that enable AI systems and product teams to reason about experience data, including ontologies, semantic systems, text analytics, prediction, simulation, and benchmarking. The position also requires managing the entire lifecycle for multiple functional areas of Understand, from problem framing to implementation and iteration, ensuring these capabilities are world-class. The ideal candidate will partner with various teams across Qualtrics to understand their needs and translate them into product strategy, requirements, and roadmaps, while staying at the forefront of AI developments.

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.
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.

Nice To Haves

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.
Experience creating products that serve internal developers, external customers, or both is a strong plus.

Responsibilities

Define the 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.
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.
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, Text analytics and enrichments, Prediction, Simulation, Benchmarking, Agent infrastructure, Agent and model evaluation, Context engineering, memory, and retrieval, AI observability and debugging, Model infrastructure and abstraction layers, Guardrails, permissions, governance, and safety.
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.