Senior Product Manager, Personalization ML Products
Senior Product Manager to lead AI/ML modeling and platform infrastructure for Klaviyo's predictive and generative AI features, optimizing models like send-time prediction and churn analysis while overseeing training pipelines, MLflow, and inference serving.
- Compensation
- $136K–$204K / yr
- Location
- Boston, MA
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Product
- Posted
Skills
Must have
- 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
Full description
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 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