Senior Product Manager, ML Modeling & Platform

*At the company, we value the unique backgrounds, experiences and perspectives each the company (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 the company? Visit the company.com/careersto see how we empower creators to own their own destiny.* About the Team the company'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, the company 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 the company — 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 the company 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. About the Role We're looking for a Senior Product Manager to own both what the company'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 the company'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 the company builds on: DART (our Ray-based offline ML job platform), MLflow, model serving, feature pipelines, and Prefect. Your job is to make the company'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. How You'll Make a Difference Own the product strategy and roadmap for the company'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 Partner with engineering leadership on build vs. buy decisions across the ML stack — ensuring the platform evolves ahead of the needs of ML and AI product teams, not reactively behind them Connect platform and modeling investments to customer and business outcomes, working with go-to-market and customer success teams to ensure AI-powered features are well-understood and improving based on real feedback Ensure platform reliability, observability, and cost efficiency for ML workloads operating across hundreds of billions of events and 200,000+ customers Reduce developer experience friction for ML and AI engineers — making it materially easier and faster to ship new models safely and maintain them in production As the company's agent products place new demands on inference infrastructure and LLM tooling, help shape how the ML platform evolves to meet those needs Who You Are You have 5+ years of product management experience, ideally owning ML, AI, or data platform products in a production environment — not just products that use AI as a feature You have working knowledge of ML systems — training pipelines, model serving, experiment tracking, feature stores, or related infrastructure — and understand the tradeoffs involved in building and operating them at scale You know how to connect internal platform investments to customer and business outcomes, and can translate "we improved training throughput by 40%" into a product story leadership and go-to-market partners actually care about You're comfortable being the only PM in a highly technical room — you know when to drive decisions, when to defer to engineers, and how to build credibility without needing to be the most technical person there You think in terms of systems and tradeoffs — model quality, inference latency, training cost, developer velocity — and can make well-reasoned prioritization decisions when they conflict You can balance long‑term platform investments with short‑term product needs, and know when to build, buy, or defer You communicate clearly and can translate complex ML and infrastructure concepts into business impact for both technical and non-technical audiences You have a track record of moving roadmaps and priorities across ML, data science, infrastructure, and product teams without direct authority Nice to Have Hands‑on prior‑career background in ML engineering, data science, or software engineering Familiarity with Ray, MLflow, Prefect, or similar distributed compute and workflow orchestration platforms Experience with LLM inference infrastructure or building products on top of generative AI and agent frameworks Exposure to high‑volume, low‑latency inference systems or large‑scale distributed training workloads Experience as a PM embedded with an ML engineering team in a B2B SaaS or high‑growth tech context Prior experience at a martech, ecommerce, or data‑forward SaaS company Familiarity with the company's product surface and the email/SMS marketing ecosystem #J-18808-Ljbffr United States Digital Space LLC