Senior Product Manager, ML Modeling & Platform New Palo Alto, CA

___

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.

Klaviyo's AI & Analytics pillar is building the intelligence layer behind the product — models that optimize send times, predict churn, surface product recommendations, and power the agentic experiences that help marketers run their business autonomously. With over 200,000+ customers, billions of consumer profiles, and hundreds of billions of messages and conversion events, Klaviyo has the data and scale to build world-class AI and machine learning products.

The ML Platform team is the engine that makes all of that possible. Every model that ships at Klaviyo — whether it's predicting the best moment to send an email or powering a Marketing Agent recommendation — runs on infrastructure this team owns. That includes training pipelines (built on Ray), experiment tracking and model registries (MLflow), inference serving infrastructure, feature pipelines, and emerging workflow orchestration tooling (Prefect).

As Klaviyo leans harder into agentic products and generative AI, the demands on this infrastructure are accelerating — and so is the team's impact.

This PM role is embedded in Palo Alto alongside our ML engineering org. You'll have the kind of proximity to your engineering team that most ML PMs only read about.

We're looking for a Senior Product Manager to own both what Klaviyo's ML models do and the platform that makes building them possible. That's a deliberate pairing: the best ML PM at this stage isn't someone who thinks exclusively about model quality or exclusively about infrastructure tooling — it's someone who can reason across both, connecting platform investment to model outcomes and model outcomes to customer impact.

On the modeling side, you'll own the roadmap for Klaviyo's core predictive models — smart send time, audience optimization, product recommendations, and churn prediction — as well as the AI-assisted content and agent capabilities that are growing in importance. You'll define what quality means for each model family, set the evaluation frameworks, and work with ML engineers and data scientists to ensure improvements compound over time.

On the platform side, you'll own the roadmap for the internal infrastructure that every ML team at Klaviyo builds on: DART (our Ray-based offline ML job platform), MLflow, model serving, feature pipelines, and Prefect. Your job is to make Klaviyo's ML engineers faster, safer, and more confident shipping models to production — and to make sure the platform stays ahead of, not behind, the pace of our AI ambitions.

This is a highly technical role. You'll be the only PM on the team, embedded with engineering in Palo Alto, and you'll need to earn credibility by speaking the language — not by knowing how to code, but by knowing enough about distributed training, inference tradeoffs, and ML developer experience to make good calls and ask the right questions.

• Own the product strategy and roadmap for Klaviyo's core predictive and generative ML models — including smart send time, audience optimization, product recommendations, and churn prediction — defining what "better" looks like and how we get there
• Own the ML Platform roadmap: training infrastructure (DART/Ray), experiment tracking (MLflow), model serving, feature pipelines, and emerging tooling (Prefect) — making time-to-production for new models a first-class metric and driving it down continuously
• Build and maintain evaluation and monitoring frameworks so model quality is measurable, regressions are caught before they reach customers, and improvements compound over time
• Part…