Interested in this AI Product Manager role at Qualtrics?
Apply Now →About This Role
Seattle, Washington, United States Category Engineering, Product, \& UX Design
JOB DESCRIPTION
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. But 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.
Principal Product Manager, Core AI Platform
Why We Have This Role
- Define the future of the core AI infrastructure that powers intelligent experiences across Qualtrics.
- Build the platform capabilities that enable product teams to create trusted, scalable, and differentiated AI experiences using structured and unstructured experience data.
- Own the product strategy for foundational AI capabilities such as ontologies and semantic systems, agent infrastructure, context and memory, tools and orchestration, agent evaluation, observability, and AI safety.
- Manage the entire lifecycle for multiple functional areas of the Core AI Platform, 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.
How You’ll Find Success
- Partner with product, engineering, data science, research, and design teams across Qualtrics to understand the infrastructure and platform capabilities required 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 platform strategy, requirements, and roadmaps.
- Define product strategy for foundational AI capabilities, including areas such as ontologies and semantic layers, agent runtimes and orchestration, tool use, context engineering, memory, model access, agent evaluation, observability, and guardrails.
- Prioritize platform investments based on customer value, 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, reliability, safety, and business impact of agentic AI systems.
- Create shared platform 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 for the Core AI Platform to senior leaders, product teams, technical stakeholders, and customers.
- Define and monitor meaningful KPIs for platform adoption, AI quality, evaluation performance, developer velocity, reliability, and customer impact.
- Stay at the forefront of developments in agents, foundation models, evaluation methods, context engineering, semantic systems, and enterprise AI infrastructure—and translate those developments into concrete product opportunities for Qualtrics.
How You’ll Grow
- By shaping the technical and product foundations for the next generation of AI experiences across Qualtrics.
- Through developing deep expertise in emerging areas such as agent architecture, ontologies, semantic systems, evaluation, and AI infrastructure.
- By making high\-leverage product decisions that influence multiple product lines and teams.
- Through leading complex, ambiguous initiatives that require alignment across product, engineering, research, data science, security, and go\-to\-market organizations.
- By developing your ability to connect rapidly evolving AI technologies to durable customer value and differentiated product strategy.
Things You’ll Do
- Develop and execute the product strategy for Qualtrics’ Core AI Platform.
- Define the foundational architecture and capabilities required for teams across Qualtrics to build reliable, differentiated AI experiences.
- Lead product strategy for areas including:
+ Ontologies, semantic layers, and grounding AI systems in the meaning and relationships within enterprise experience data.
+ Agent infrastructure, including orchestration, planning, tool use, delegation, and multi\-agent patterns.
+ Agent evaluation, including offline and online evaluation systems, task success measurement, quality frameworks, regression testing, and human\-in\-the\-loop evaluation.
+ Context engineering, memory, retrieval, and 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 that allow Qualtrics teams to use the right models for the right tasks while managing cost, latency, reliability, and quality.
+ Guardrails, permissions, governance, and safety systems required for trusted enterprise AI.
- Work closely with product teams across Qualtrics to understand their AI use cases and identify opportunities for shared platform capabilities.
- Connect with enterprise customers to understand their expectations for trusted, explainable, governed, and reliable AI systems.
- Discover and prioritize platform 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, platform roadmaps, and measurable outcomes.
- Manage complex cross\-functional work across product, engineering, AI research, data science, UX design, and research.
- Create strong platform adoption strategies so that new infrastructure is easy to discover, easy to use, and meaningfully improves the speed and quality of AI product development.
- Know the technical landscape, platform adoption metrics, AI quality measures, and emerging market trends better than anyone.
- Lead the rollout of new AI platform capabilities, including internal adoption, developer enablement, documentation, customer communication, and external positioning where appropriate.
What We’re Looking For On Your Resume
- 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, infrastructure products, AI/ML products, developer platforms, or data systems.
- 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 areas is strongly preferred:
+ Agentic AI systems and agent orchestration.
+ AI or agent evaluation systems.
+ Ontologies, knowledge graphs, semantic layers, or metadata systems.
+ 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.
- Ability to operate comfortably at both the strategic and technical levels, from articulating a multi\-year platform 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 platform products that serve internal developers, external customers, or both is a strong plus.
What You Should Know About This Team
- The Core AI Platform team at Qualtrics builds the foundational intelligence layer that 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 will require infrastructure that gives AI systems the right context, understanding, tools, evaluation, permissions, and feedback loops to reliably accomplish meaningful work.
- The team is building shared capabilities across ontologies and semantic systems, agents, orchestration, context and memory, evaluation, observability, model infrastructure, and enterprise AI governance.
- We are seeking a Principal Product Manager who can help define this platform, 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 product portfolio to understand the AI experiences they want to create, identify common infrastructure needs, and build reusable capabilities that increase the speed, quality, and ambition of AI development across the company.
Our Team’s Favorite Perks and 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.
The Qualtrics Hybrid Work Model: Our hybrid work model is elegantly simple: we all gather in the office three days a week; Mondays and Thursdays, plus one day selected by your organizational leader. These purposeful in\-person days in thoughtfully designed offices help us do our best work and harness the power of collaboration and innovation. For the rest of the week, work where you want, owning the integration of work and life. \#hybrid
*Qualtrics is an equal opportunity employer meaning that all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other protected characteristic.*
*Applicants in the United States of America have rights under Federal Employment Laws:*Family \& Medical Leave Act*,*Equal Opportunity Employment*,*Employee Polygraph Protection Act
*Qualtrics is committed to the inclusion of all qualified individuals. As part of this commitment, Qualtrics will ensure that persons with disabilities are provided with reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please let your Qualtrics contact/recruiter know.*
*Not finding a role that’s the right fit for now? Qualtrics Insiders is the one\-stop shop for all things Qualtrics Life. Sign up for exclusive access to content created with you in mind and get the scoop on what we have going on at Qualtrics \- upcoming events, behind the scenes stories from the team, interview tips, hot jobs, and more. No spam \- we promise! You'll hear from us two times a month max with fresh, totally tailored info \- so be sure to stay connected as you explore your best role and company fit.*
\#AI
*For full\-time positions*, this pay range is for base per year; however, base pay offered within this range may vary depending on location, job\-related knowledge, education, skills, and experience. A sign\-on bonus and restricted stock units may be included in an employment offer. 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.
Washington State Base Annual Pay Transparency Range
$199,500—$262,000 USD
Salary Context
This $199K-$262K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Qualtrics, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($230K) sits 7% above the category median. Disclosed range: $199K to $262K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Qualtrics AI Hiring
Qualtrics has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist. Positions span Seattle, WA, US, Reston, VA, US. Compensation range: $262K - $262K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.