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Staff Engineer, ML/AI Platform
AttentiveUnited States$260k – $310kPosted 20 May 2026
Tech Stack
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!), Inc.’s Best Workplaces , and the Human Rights Campaign Foundation's Corporate Equality Index !
About the Role We’re seeking an accomplished Staff Software Engineer to join Attentive’s Machine Learning Platform team as a high-impact individual contributor focused on building the AI and ML infrastructure that powers our AI product suite. You’ll architect and build the foundational platform components that enable AI / ML engineers and data scientists to train, deploy, and serve models and agentic infrastructure with velocity, performance, and reliability at scale. As a Staff-level IC, you’ll operate as a technical force multiplier, setting the technical direction for AI and ML infrastructure across Attentive’s AI organization. You’ll lead through influence and technical excellence, advocating for long-term architectural progress while balancing immediate platform needs. Your work will span strategic initiatives measured in quarters and years, focusing on high-leverage decisions that enable entire teams to ship AI and ML capabilities faster and more reliably. Strategic Need Attentive is revolutionizing the digital shopping experience across every channel through our AI product suite for half a billion subscribers. We’re looking for a high impact individual to take our platform from v1 to vNext and beyond — supporting the full spectrum of AI and ML workloads at massive scale. We support traditional models and deep learning today, and we are growing into reinforcement learning and agentic infrastructure quickly. This is a ground-floor opportunity to drive and influence the architectural roadmap for Attentive’s entire AI and ML ecosystem toward self-service workflows, real-time inference at scale, agentic capabilities, and robust model lifecycle management.
What You’ll Accomplish
Setting Technical Direction - Architect ML platform strategy spanning data pipelines, training infrastructure, and serving layers using cutting-edge tooling like Ray, MLFlow, Metaflow, Argo, and Spark.
Uplevel and Innovate Core AI ML Stack - Build and operate production-grade, low-latency ML serving layers with robust model lifecycle systems including champion/challenger testing, automated rollouts, versioning, and rollback capabilities.
Uplevel and Innovate Core AI ML Stack - Define and drive Attentive’s agentic stack.
Technical Leadership - Provide ML infrastructure perspective in high-level discussions about Attentive’s AI strategy spanning multiple quarters and teams.
Technical Mentorship - Mentor platform and ML engineers, actively championing team members.
Being the “Glue” - Build universal interfaces, architectures, and patterns—like data access layers and prediction serving APIs—that bridge platform capabilities with product needs to streamline high-priority ML work across the organization.
Your Expertise
You have the experience to know what works, what doesn ... (truncated, view full listing at source)
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