Staff Software Engineer, ML/AI Platform

Attentive is an AI marketing platform that focuses on 1:1 personalization to enhance customer relationships. They are looking for a Staff Software Engineer to join their Machine Learning Platform team, responsible for building the AI and ML infrastructure that supports their product suite. The role involves architecting foundational platform components and leading technical direction to enhance AI and ML capabilities across the organization.

Responsibilities:

• 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

Requirements:

• 7+ years focused specifically on ML Platform/MLOps, with deep understanding of gold-standard practices and best-in-class tooling
• Proven track record of owning and building core components of ML platforms using tools like Spark, Ray, MLFlow, Kubeflow, or Metaflow
• Strong expertise in Python for both batch processing and online service frameworks
• Experience designing and operating online and offline inference systems, understanding the critical differences and tradeoffs between them
• You have the experience to know what works, what doesn't, and why in AI and ML systems