Senior Product Manager, Personalization ML Products

At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. If you’re a close but not exact match with the description, we hope you’ll still consider applying. Want to learn more about life at Klaviyo? Visit klaviyo.com/careers to see how we empower creators to own their own destiny. About the Team Klaviyo's AI & Analytics pillar builds the intelligence layer that helps marketers understand their customers, personalize every interaction, and prove the impact of their work. Two of our highest-leverage products live at the center of this: Product Recommendations and Audience Optimization. Both are ML-powered. Both exist for one reason: to help brands running autonomous marketing get a real, measurable lift in performance and revenue. Klaviyo runs like an ecosystem of startups. Every team owns real outcomes, and the people who succeed here are builders who pull in the partners they need, even when those partners don't report to them. This role is a clear example of that model in action. About the Role We're looking for a Senior Product Manager to own the product and model strategy for two connected feature areas: Product Recommendations and Message Prioritization, with room to take on additional ML-powered feature pods as the portfolio grows. Product Recommendations spans multiple model types, deep familiarity with product catalog data structures, and enterprise-grade customization: filters, guardrails, exclusions, and the business rules brands need layered on top of a model before they'll trust it in production. Message prioritization decides who receives a message, which message they receive, and when. Any given profile might be eligible for several messages in a short window. This feature caps how many they actually get and picks the ones most likely to convert, so the model has to optimize for revenue and conversion at the same time it respects frequency limits. Across both areas, this PM owns the full outcome: not just what ships, but whether it delivers positive lift for the brands using it. That means you're accountable for model performance the same way you're accountable for the product experience wrapped around it. How You'll Make a Difference • Own the roadmap for Product Recommendations, including model type selection, catalog data structure decisions, and enterprise-grade customization such as filters, guardrails, and exclusions • Own the roadmap for Message Prioritization, defining how the model balances message frequency limits against conversion and revenue outcomes • Define what model success looks like for each feature area and build the frameworks to measure lift, conversion improvement, and revenue impact over time • Partner closely with ML modeling teams and software engineering teams to translate model capability into features customers actually trust and adopt • Design product experiences that make ML-driven decisions understandable and explainable to marketers, so the model never feels like a black box • Influence teams beyond your direct authority to align on priorities and deliver feature outcomes, consistent with how Klaviyo's product orgs operate • Take full ownership of model and feature performance after launch. Shipping is the start of the job, not the end of it • Build a clear point of view on why brands choose to pay for ML-powered personalization features, and use that to shape what gets built next • Grow into ownership of additional ML-powered feature pods as Klaviyo's personalization portfolio expands Who You Are • 3–5+ years of PM experience owning ML-powered or data-driven product features in a production environment, not just AI-adjacent products • Direct experience evaluating and tracking ML model performance: you understand lift measurement, experiment design, and what good model quality tracking looks like • Comfortable partnering with both software engineering and ML modeling or data science teams. You speak enough of both languages to make good calls without needing everything explained to you • Genuine customer obsession: you understand why a brand decides to pay for an ML feature, and you translate that understanding directly into roadmap decisions • Strong instincts for human behavior and UX: you know how to design trust, clarity, and adoption into a feature that could otherwise feel opaque or unpredictable • Comfortable working with complex data structures, like product catalogs, and turning that complexity into simple, understandable customer-facing controls • High agency: you take ownership of outcomes, not just deliverables, and you pull in the partners you need even when they don't report to you • A track record of driving cross-functional alignment and shipping through ambiguity, without relying on direct authority to get it done Nice to Have • Experience with recommendation systems or catalog-driven ML products • Experience with message optimization, cadence, or frequency-capping problems • Background in martech, ecommerce, or CDP products • Experience building enterprise-grade customization layers (filters, guardrails, business rules) on top of ML models • Experience as an embedded PM partner to a data science or ML engineering team Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Our salary range reflects the cost of labor across various U.S. geographic markets. The range displayed below reflects the minimum and maximum target salaries for the position across all our US locations. The base salary offered for this position is determined by several factors, including