Job 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 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, ethnicity, citizenship, national origin, color, religion or religious creed, age, sex (including pregnancy), gender identity, sexual orientation, physical or mental disability, veteran or active military status, marital status, criminal record, genetics, retaliation, sexual harassment or any other characteristic protected by applicable law. IMPORTANT NOTICE: Our company takes the security and privacy of job applicants very seriously. We will never ask for payment, bank details, or personal financial information as part of the application process. All our legitimate job postings can be found on our official career site. Please be cautious of job offers that come from non-company email addresses (@klaviyo.com), instant messaging platforms, or unsolicited calls. By clicking "Submit Application" you consent to Klaviyo processing your Personal Data in accordance with our Job Applicant Privacy Notice. If you do not wish for Klaviyo to process your Personal Data, please do not submit an application. You can find our Job Applicant Privacy Notice here and here (FR).

Requirements

Must-haves

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
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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiml

Platform: greenhouse
Effort: quick