About the job
At Attentive®, we are revolutionizing AI-driven marketing through personalized 1:1 connections that transform the way brands engage with consumers. Our innovative platform uniquely integrates cutting-edge technology and human expertise, fostering genuine customer relationships. By leveraging SMS, RCS, email, and push notifications, our AI-enhanced personalization engine crafts tailored experiences that boost performance, revenue, and customer loyalty through real-time behavioral insights.
As the #1 SMS Marketing provider recognized by G2, Attentive proudly collaborates with over 8,000 clients across more than 70 industries. Esteemed global brands such as Crate and Barrel, Urban Outfitters, and Carter’s partner with us to facilitate billions of interactions, generating substantial revenue for our clientele.
With a globally distributed team and hubs located in New York City, San Francisco, London, and Sydney, Attentive’s workforce has consistently garnered accolades for excellence and culture. We are honored to be featured in Deloitte’s Fast 500 for four consecutive years, LinkedIn’s Top Startups, Forbes’ Cloud 100 for five years running, and Inc.’s Best Workplaces.
About the Role
Our Machine Learning Engineering team is at the forefront of creating personalized experiences for millions of customers across a diverse array of brands. We develop sophisticated ML models that anticipate customer behaviors in real-time, enabling hyper-personalized shopping journeys. Joining our team means stepping into a dynamic career opportunity where you will collaborate with some of the industry’s brightest machine learning engineers in a high-performance culture that emphasizes impact and growth.
We are in search of a proactive and highly skilled Principal Machine Learning Engineer to join our expanding teams. As a key early hire, you will play a pivotal role in the development of machine learning models and foundational infrastructure across the Attentive platform, collaborating closely with Product Management and Engineering teams to implement comprehensive modeling use cases.