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Overview:
Medallia is the pioneer and market leader in Experience Management. Our award\-winning SaaS platform, Medallia Experience Cloud, leads the market in the management of experiences, insights, and actions for candidates, customers, employees, patients, and residents alike.
We believe that every experience is a memory that can last a lifetime. Experiences shape the way people feel about a company. And they greatly influence how likely people are to advocate, contribute, and stay. At Medallia, we are committed to creating a world where organizations are loved by their customers and their employees.
We empower exceptional people to create extraordinary experiences together.
Bring your whole self.
The Role and Team
We are seeking an experienced and visionary Principal Platform Product Manager for our AI Platform. As part of our Data and AI Platform Product Team, this role sits at the intersection of Responsible AI, model lifecycle management, and user trust. You will shape core capabilities that ensure our AI systems are performant, reliable, transparent, and aligned with both user expectations and regulatory requirements.
Taking initiative and operating independently, requiring limited to no support, you will drive the strategy, development, and growth of our AI platform capabilities, ensuring that it meets the evolving needs of our customers and partners, and maintains our competitive edge in the market. As a Principal PM, you will step in to complement the existing team by taking ownership of high\-impact, complex product areas spanning languages, usage/pricing, model flexibility, and explainability.
Your work will directly impact how AI and Generative AI capabilities are governed, scaled, and experienced across Medallia’s customer and employee experience products. You will collaborate closely with Engineering, Design, Compliance, and Research to deliver platform services that enable faster innovation while ensuring trust, safety, and consistency. This is a highly cross\-functional and impactful role, ideal for someone who thrives on systems thinking, product craftsmanship, and ethical leadership in AI.
Candidates based in the Tysons vicinity will be prioritized as this role is Hybrid, 3 days per week onsite.
Responsibilities:
- Drive product strategy and delivery of Medallia's AI Platform as part of our Experience Platform, focusing on scalable infrastructure, AI/ML frameworks, model extension, administration, and seamless integration with internal and external applications.
- Define and own the roadmap for critical cross\-product and platform workstreams.
- Be able to take full ownership of areas such as our models and their lifecycle to alleviate team bottlenecks and drive market innovation.
- Spearhead the product strategy and operationalization of AI Consumption, Usage tracking, and Pricing mechanisms.
- Define, communicate, and enforce non\-functional requirements (NFRs) for AI/ML services such as model SLAs, degradation monitoring, rate limiting, and incident management to ensure high\-availability and reliable AI/ML services.
- Develop tools and dashboards that enable auditability and behavioral evaluation of AI systems.
- Define and enforce standards for Responsible AI, including traceability, explainability, and alignment with evolving regulatory frameworks including configuration and administration tooling for select GenAI and AI platform features.
- Partner with Design and Product teams to ensure consistent and user\-friendly AI experiences, including transparency prompts, user control points, and override flows.
- Collect and analyze feedback from customers, stakeholders and other teams to shape requirements, features and end products.
- Engage with cross\-functional teams, including PM, Design, Engineering, Sales, Professional Services, and Marketing, to ensure effective collaboration and alignment.
Qualifications:
Minimum Qualifications* Bachelor's Degree in science, engineering or related quantitative field, or equivalent experience.
- 8\+ years of enterprise SaaS Product and/or Engineering management experience focused on data, analytics, and/or ML/AI products and services.
- Demonstrated experience managing cross\-functional initiatives involving AI model performance, feedback systems, or Responsible AI tooling.
- Demonstrated understanding of ML pipelines, model serving, and feedback loop integration.
- Demonstrated understanding of distributed systems and cloud\-native architectures.
- Demonstrated experience driving cross\-functional company initiatives across multiple departments.
Preferred Qualifications* 5\+ years of direct and hands\-on experience bringing enterprise AI/ML platforms and/or products to the market from ideation to shipping the capabilities.
- Demonstrated experience building, launching, and scaling enterprise\-grade AI platform solutions.
- Deep understanding of Responsible AI practices including explainability, model cards, auditing frameworks, and policy alignment.
- Self\-starter and able to operate independently to take an initiative from start to finish, while being comfortable with ambiguity and navigating complex execution challenges with the right level of cross\-functional collaboration.
- Experience working with R\&D and Research teams to productionalize ML/GenAI capabilities.
- Familiarity with user experience design for AI features, including user transparency, consent flows, and control mechanisms.
- Proven ability to define non\-functional requirements (SLAs, observability, rate limiting) for AI/ML services and implement appropriate tooling and mitigation efforts.
- Experience with a Customer Experience Management Platform.
- Experience working in an agile development environment and familiarity with tools such as Productboard and JIRA.
Medallia is committed to equal pay and transparency. The annual base salary range for this position is $186,000 \- $260,000 USD. This position is bonus eligible. Please note that the salary range information provided is a general guideline and combines all of the distinct labor markets within the US. It is uncommon for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on a variety of factors. Medallia considers factors such as (but not limited to) scope and responsibilities of the position, candidate’s work experience, candidate’s work location, education/training, key skills, internal peer equity, external market data, as well as, market and business considerations when making compensation decisions.
Medallia also offers competitive health and wellness benefits, including but not limited to medical, dental, vision, 401(k), short\-term and long\-term disability, life and AD\&D insurance, statutory leaves, paid parental leave, and paid holidays. Benefits and eligibility may vary by location and role.
At Medallia, we celebrate diversity and recognize the value it brings to our customers and employees. Medallia is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age (40 and over), disability, genetic information, veteran status or military service, or any other status protected by state or local law. Individuals with a disability who need an accommodation to apply please contact us at [email protected]. For information regarding how Medallia collects and uses personal information, please review our Privacy Policies. Applications will be accepted for 30 days from the date this role was posted or until the role has been filled.
Salary Context
This $186K-$260K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $189K across 161 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 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Medallia, 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 $213,800 based on 583 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $186K to $260K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Medallia AI Hiring
Medallia has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in McLean, VA, US. Compensation range: $260K - $360K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
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