About The Position

Join us as an ML Data and Infrastructure Engineer and become the architect behind the data infrastructure that power tomorrow's breakthrough AI/ML innovations. You'll be the critical link between ambitious algorithmic vision and real-world implementation—building the robust, scalable infrastructure that turns cutting-edge research into production-ready systems. We don't just use tools; we build them. You'll have complete ownership of the infrastructure that fuels our ML algorithms from conception to deployment, designing and orchestrating distributed compute systems that process massive datasets at scales few engineers ever encounter. Working shoulder-to-shoulder with AI/ML researchers and engineers in a small, agile team, you'll drive the creation of scalable infrastructure for ground truth data delivery, orchestrate massive distributed compute tasks across petabyte-scale datasets, develop novel validation frameworks, and help define strategic data collection approaches that push the boundaries of what's achievable. Your contributions won't just support algorithms—they'll directly shape product direction, unlock entirely new AI/ML capabilities, and define what's possible at Apple.

Requirements

  • BS in computer science or related discipline and 3+ years of relevant industry experience
  • Experience developing or extending frameworks used for automating pipelines
  • Strong software engineering skills with extensive experience in Python
  • Strong foundational knowledge in Computer Science, with a deep understanding of algorithms and data structures

Nice To Haves

  • MS or PhD computer science or related discipline, or 5+ years of related industry experience
  • Experience processing large, complex, unstructured data
  • Experience developing core infrastructure and frameworks for automating data pipelines
  • Excellent communication and experience working with multi-functional teams
  • Passion for delivering high quality software to end-users
  • Experience with geometry or computer vision algorithms
  • Self-motivated, with an ability to drive projects from concept to production, balancing requirements with technical quality and development timelines

Responsibilities

  • drive the creation of scalable infrastructure for ground truth data delivery
  • orchestrate massive distributed compute tasks across petabyte-scale datasets
  • develop novel validation frameworks
  • define strategic data collection approaches
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