What you'll do - Own the product strategy and roadmap for Klaviyo's core predictive and generative ML models (e.g., smart send time, audience optimization, product recommendations, churn prediction) and the AI-assisted content and agent capabilities. - Own the ML Platform roadmap: training infrastructure (DART/Ray), experiment tracking (MLflow), model serving, feature pipelines, and emerging tooling (Prefect). - Build and maintain evaluation and monitoring frameworks to measure model quality, catch regressions, and ensure improvements compound over time. - Partner with engineering leadership on build vs. buy decisions across the ML stack, ensuring the platform evolves ahead of needs. - Connect platform and modeling investments to customer and business outcomes, coordinating with go-to-market and customer success teams to drive adoption and impact. - Ensure platform reliability, observability, and cost efficiency for ML workloads across hundreds of billions of events and 200,000+ customers. - Reduce developer friction for ML and AI engineers to ship models safely and maintain them in production. - Help shape how the ML platform evolves to meet the demands of agent products, inference infrastructure, and LLM tooling. - You will be the sole PM on the team, working closely with engineers; you’ll need to speak the language of distributed training, inference

What you'll do - Own the product strategy and roadmap for Klaviyo's core predictive and generative ML models (e.g., smart send time, audience optimization, product recommendations, churn prediction) and the AI-assisted content and agent capabilities. - Own the ML Platform roadmap: training infrastructure (DART/Ray), experiment tracking (MLflow), model serving, feature pipelines, and emerging tooling (Prefect). - Build and maintain evaluation and monitoring frameworks to measure model quality, catch regressions, and ensure improvements compound over time. - Partner with engineering leadership on build vs. buy decisions across the ML stack, ensuring the platform evolves ahead of needs. - Connect platform and modeling investments to customer and business outcomes, coordinating with go-to-market and customer success teams to drive adoption and impact. - Ensure platform reliability, observability, and cost efficiency for ML workloads across hundreds of billions of events and 200,000+ customers. - Reduce developer friction for ML and AI engineers to ship models safely and maintain them in production. - Help shape how the ML platform evolves to meet the demands of agent products, inference infrastructure, and LLM tooling. - You will be the sole PM on the team, working closely with engineers; you’ll need to speak the language of distributed training, inference