Staff Machine Learning Engineer, Team Lead - Attentive

Job Title: Staff Machine Learning Engineer, Team Lead
Job Location: San Francisco, CA
Job Listing URL: https://jobs.lever.co/attentive/746190ef-5566-498b-a379-28d9e14974b3
Job Description: 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
Our Machine Learning Engineering team powers personalized experiences for hundreds of millions of customers across thousands of brands. We build advanced ML models that predict customer behaviors in real-time, enabling highly personalized shopping experiences. Joining our team offers a high-growth career opportunity to work with some of the world’s most talented machine learning engineers in a high-performance and high-impact culture.

We are seeking a self-driven and highly motivated Machine Learning Engineer to join our growing machine learning teams. As an early hire, you will contribute to the development of machine learning models and infrastructure needs across the Attentive platform and work with Product Management and Engineering to implement end-to-end modeling use cases.
What You'll Accomplish
You have a proven track record of building systems that maintain a high bar of quality
You deeply loathe regressions and take proactive steps to protect against them through a variety of testing techniques
You are a collaborator, technical leader, and a great communicator
You are constantly improving the quality of the project you are working on, both via direct contributions as well as long-term advocacy for larger-scale changes
You are enthusiastic about the high impact, fast-paced work environment of an late-stage startup
10+ years experience is ideal
Your Expertise
You have worked professionally building systems for 6+ years with experience on a single system long enough to see the consequences of your decisions
Experience with TensorFlow/Pytorch, xgboost, pandas, matplotlib, SQL, Spark or similar tools
You have proficiency or experience with PythonYou have extensive experience using machine learning and data analysis, or similar, to build scalable systems and data-driven products, working with cross-functional teams
You have a proven track record of building scalable, efficient, automated processes for large-scale data analyses, model development, model validation, and model implementation from modern research
You have led cross-functional machine learning projects across teams
What We Use
Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS
Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm
Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS
Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and PlaywrightOur automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas