Senior Software Engineer, ML/AI 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!), Inc.’s Best Workplaces , and the Human Rights Campaign Foundation's Corporate Equality Index ! About the Role We’re looking for a self-motivated, highly driven Senior Software Engineer to join our Machine Learning Platform (MLPlatform) team. As a team, we enable Attentive’s Machine Learning (ML) practice to directly impact Attentive’s AI product suite through the tools to train, serve, and deploy ML models with higher velocity and performance, while maintaining reliability. We build and maintain a foundational ML platform that spans the full ML lifecycle for use by ML engineers and data scientists. This is an exciting opportunity to join a rapidly growing ML Platform team at the ground floor, with the ability to drive and influence the architectural roadmap, enabling the entire ML organization at Attentive. This team and role are responsible for building and operating the ML data, tooling, serving, and inference layers of the ML platform. We are excited to bring on more engineers to continue expanding this stack. What You’ll Accomplish Unlock offline real-time access to trillions of data points for our ML and Data Science teams. Manage, expand, and optimize our feature store that enables feature engineering, multi-TB scale training jobs, and offline / real-time inferencing. Support PB scale data operations on the feature store using Apache Spark, Spark Structured Streaming, Kafka, and Ray. Partner with other teams and business stakeholders to deliver ML and AI initiatives. Your Expertise You have been working in the areas of Data Engineering / MLOps for 5+ years, and have built and matured the pipelines of a PB-scale feature store. You have deep Apache Spark, Spark Streaming, and Ray Data experience and built data pipelines for ML use cases using these tools. You understand the correlation between data cardinality, query plans, configuration settings, and hardware and the impact of each on data pipeline performance . You know/have created infrastructure for Training ML models/fine-tuning LLMs. You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each to meet business needs. 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 Playwright. Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas. You'll get competitive perks and benefits , from ... (truncated, view full listing at source)