Senior Product Manager, ML Modeling & Platform

Klaviyo•Palo Alto, CA

8d•$136,000 - $204,000•Hybrid

About The Position

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. The ML Platform team is the engine that makes all of that possible. Every model that ships at Klaviyo runs on infrastructure this team owns. This 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. This is 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. 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.

Requirements

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
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
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
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
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
Can balance long-term platform investments with short-term product needs, and know when to build, buy, or defer
Communicate clearly and can translate complex ML and infrastructure concepts into business impact for both technical and non-technical audiences
Have a track record of moving roadmaps and priorities across ML, data science, infrastructure, and product teams without direct authority

Nice To Haves

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 Klaviyo's product surface and the email/SMS marketing ecosystem

Responsibilities

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
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 Klaviyo's agent products place new demands on inference infrastructure and LLM tooling, help shape how the ML platform evolves to meet those needs