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 the applicant’s job-related skills, relevant experience, education or training, and work location.

In addition to base salary, our total compensation package may include participation in the company’s annual cash bonus plan, variable compensation (OTE) for sales and customer success roles, equity, sign-on payments, and a comprehensive range of health, welfare, and wellbeing benefits based on eligibility.

Your recruiter can provide more details about the specific salary/OTE range for your preferred location during the hiring process.

Base Pay Range For US Locations:

$136,000 — $204,000 USD

This role may require up to 10% travel for purposes such as new hire onboarding, client or partner work if applicable, team meetings, and industry events. Travel is coordinated in advance.

Get to Know Klaviyo

We’re Klaviyo (pronounced clay-vee-oh). We empower creators to own their destiny by making first-party data accessible and actionable like never before. We see limitless potential for the technology we’re developing to nurture personalized experiences in ecommerce and beyond. To reach our goals, we need our own crew of remarkable creators—ambitious and collaborative teammates who stay focused on our north star: delighting our customers. If you’re ready to do the best work of your career, where you’ll be welcomed as your whole self from day one and supported with generous benefits, we hope you’ll join us.

AI fluency at Klaviyo includes responsible use of AI (including privacy, security, bias awareness, and human-in-the-loop). We provide accommodations as needed.

By participating in Klaviyo’s interview process, you acknowledge that you have read, understood, and will adhere to our Guidelines for using AI in the Klaviyo interview Process . For more information about how we process your personal data, see our Job Applicant Privacy Notice .

Klaviyo is committed to a policy of equal opportunity and non-discrimination. We do not discriminate on the basis of race,