Staff Engineer, ML/AI Platform

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

• Experience to know what works, what doesn’t, and why in AI and ML systems.
• 5+ 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.
• Built and operated a high-throughput agentic stack (MCP / data infrastructure, context store, orchestration, and prompt layer).
• 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.

Sample Projects

• Design and implement inference pipelines with champion/challenger shadow testing and automated model promotion.
• Lead and scale Attentive’s agentic stack from the ground up.
• Scale real-time feature streaming to handle low-latency, high-volume reinforcement learning workloads.
• Build a universal data access layer and prediction serving interface that powers ML capabilities across Attentive’s product suite.