Senior Machine Learning Engineer

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!), Inc.’s Best Workplaces , and the Human Rights Campaign Foundation's Corporate Equality Index ! About the Role Our Machine Learning Engineering team powers personalized experiences for hundreds of millions of customers across thousands of brands. As a Senior Machine Learning Engineer, you will play a critical role in building, scaling, and operating production-grade ML systems that drive real-time personalization across the Attentive platform. You will operate with a high degree of ownership, partner closely with Product and Engineering, and help raise the technical bar across our ML systems in a fast-paced, high-impact environment. What You’ll Accomplish Design, build, and operate scalable machine learning systems used in real-time targeting, decisioning, personalization systems Own ML projects end-to-end, from data exploration and modeling to deployment, monitoring, and iteration Proactively safeguard model and system quality through testing, monitoring, validation, and alerting Collaborate cross-functionally with product, data, and other engineering partners to translate business problems into ML solutions Continuously improve system reliability, performance, and engineering efficiency Contribute to technical direction and best practices across the ML engineering team Mentor and support other engineers through code reviews, design discussions, and knowledge sharing Thrive in a high-impact, fast-paced, late-stage startup environment Your Expertise 6+ years of professional experience building production machine-learning software systems Proven experience owning ML systems long enough to see the downstream impact of design decisions Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or xgboost Experience with data processing and analytics tools such as pandas, Spark, SQL, and matplotlib Hands-on experience building automated pipelines for data processing, model training, validation, and deployment Experience collaborating with cross-functional teams to deliver ML-powered features Strong communication skills and a high sense of ownership What We Use Our backend is Java / Kotlin / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Aurora, AirFlow, Postgres, and Redis, hosted via AWS Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS, using tooling like Istio, Datadog, Terraform, CloudFlare, and Helm Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright Our automation is driven by custom and open source machine learning models, industry-leading LLMs, lots of data and tech like Python, Metaflow, HuggingFace, PyTorch, TensorFlow, and Pandas 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 salar