Staff Engineer, Ml/ai Platform

Attentive Attentive · Enterprise · United States · Engineering

Staff Software Engineer, ML/AI Platform at Attentive, focusing on building and scaling the AI/ML infrastructure for their AI product suite. The role involves architecting and developing foundational platform components for training, deploying, and serving models and agentic infrastructure, with a focus on velocity, performance, and reliability at scale. Key responsibilities include setting ML platform strategy, building low-latency ML serving layers, defining the agentic stack, and providing technical leadership.

What you'd actually do

  1. 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.
  2. 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.
  3. Uplevel and Innovate Core AI & ML Stack - Define and drive Attentive’s agentic stack.
  4. Technical Leadership - Provide ML infrastructure perspective in high-level discussions about Attentive’s AI strategy spanning multiple quarters and teams.
  5. Technical Mentorship - Mentor platform and ML engineers, actively championing team members.

Skills

Required

  • ML Platform/MLOps
  • Python
  • Spark
  • Ray
  • MLFlow
  • Kubeflow
  • Metaflow
  • Argo
  • online and offline inference systems
  • agentic stack

Nice to have

  • champion/challenger testing
  • automated rollouts
  • versioning
  • rollback capabilities
  • data access layers
  • prediction serving APIs

What the JD emphasized

  • 5+ years focused specifically on ML Platform/MLOps
  • Proven track record of owning and building core components of ML platforms
  • You’ve built and operated a high-throughput agentic stack

Other signals

  • ML Platform
  • MLOps
  • Agentic Infrastructure
  • Real-time Inference
  • Model Lifecycle Management