Engineering Manager, ML Platform

Attentive® is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights. Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers. With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in Deloitte’s Fast 500 (four years running!), LinkedIn’s Top Startups , Forbes’ Cloud 100 (five years running!), and Inc.’s Best Workplaces . About the Role Attentive is building the next generation of ML-driven personalization and decisioning for messaging and marketing. We are looking for an ML Platform Engineering Manager to build and lead our Machine Learning Platform team. This team owns the foundational systems that enable our ML engineers and data scientists to train, deploy, and maintain production-grade ML models at massive scale. You will define the platform roadmap, hire and grow the team, and deliver the tooling and infrastructure that makes ML work repeatable, observable, and easy to operate at scale. This role reports directly to the Head of Machine Learning. What You’ll Accomplish Build and lead the ML Platform team (hiring, coaching, execution bar) Own the end-to-end ML platform roadmap and delivery: training + evaluation infrastructure, feature management, and standardized ML workflows Ship a clear “golden path” for ML development: CI/CD, champion/challenger rollouts, experimentation, model registry, and automated re-training Enable massively scalable deployments (batch and near-real-time), including rollout patterns (shadow/canary), robust contracts, and operational readiness (SLOs, runbooks, on-call) Lead ML observability and debugging across the stack (data quality, drift, performance, latency, cost), leveraging Ray + AnyScale Partner across ML Eng, Data Science, Analytics Eng, and Infrastructure to increase velocity and develop a world-class standard for Machine Learning integrations Drive cost and capacity efficiency for distributed compute (scheduling, resource governance, spend visibility) Your Expertise 6+ years in software/data/ML-infra engineering, including 2+ years people management; experience building shared platforms adopted by multiple ML teams Strong distributed systems + cloud fundamentals; comfortable owning reliability (SLOs, incidents, on-call maturity) Production ML platform experience across the lifecycle: feature pipelines, training/eval, deployment, and monitoring Snowflake-native experience for modeling-ready data and feature management (data modeling, backfills, point-in-time correctness, governance/lineage) nice to have Hands-on experience with distributed computing (Ray, Spark, Dask) with Ray and/or AnyScale for distributed workloads and observability Familiar with orchestration (Metaflow, Airflow, Dagster, etc., data transformation pipelines (dbt), containerization (Docker/Kubernetes), and modern CI/CD practices Experience with ML observability and lifecycle management (MLflow) Clear technical leadership: roadmap setting, stakeholder alignment, and ability to get into the weeds when needed You'll get competitive perks and benefits , from health wellness to equity, to help you bring your best self to work. For US based applicants: The US base salary range for this full-time position is $227,000 - ... (truncated, view full listing at source)