๐ Local Job Near You
ML Compute Efficiency Automation Engineer, Infrastructure & Planning
Apple
๐
Cupertino, United States
Location
Cupertino
Posted
June 21, 2026
Commute
Local Area
Local Opportunity Near You!
This job is in your area. Enjoy a short commute and work close to home.
Job Description
**Role Number:** 200669000-0836
**Summary**
Appleโs Platform Acceleration & Compute Efficiency (PACE) is a high-leverage team operating at the intersection of our ML organizations, underlying compute infrastructure, and core platform tooling. Our mission is to empower Appleโs software engineering teams with efficient, scalable compute. By driving out operational friction and optimizing the broader machine learning ecosystem, we directly accelerate the pace of development for our Software and AIML organization.
Foundation models are central to Apple's user experiences and maximizing the efficiency of our ML compute is paramount. Compute efficiency sits at the center of this role, ensuring that Appleโs models run as fast, reliably, and cost-effectively as possible. In this role you will tackle optimization challenges, from maximizing hardware utilization across GPUs, TPUs, and custom Apple Silicon, to shaping workload scheduling and capacity allocation for large model ser...
**Summary**
Appleโs Platform Acceleration & Compute Efficiency (PACE) is a high-leverage team operating at the intersection of our ML organizations, underlying compute infrastructure, and core platform tooling. Our mission is to empower Appleโs software engineering teams with efficient, scalable compute. By driving out operational friction and optimizing the broader machine learning ecosystem, we directly accelerate the pace of development for our Software and AIML organization.
Foundation models are central to Apple's user experiences and maximizing the efficiency of our ML compute is paramount. Compute efficiency sits at the center of this role, ensuring that Appleโs models run as fast, reliably, and cost-effectively as possible. In this role you will tackle optimization challenges, from maximizing hardware utilization across GPUs, TPUs, and custom Apple Silicon, to shaping workload scheduling and capacity allocation for large model ser...