Machine Learning Compute Efficiency Lead, Infrastructure & Planning

What you'll do

• Own and optimize ML compute management for Apple’s inference workloads across various hardware.
• Develop and implement resource strategies to address pain points identified by ML engineering teams.
• Drive performance improvements and reduce service costs through deep analysis of root causes.
• Architect solutions for large-scale optimization problems, including capacity allocation and workload scheduling.
• Advocate for ML engineers’ needs in interactions with internal infrastructure providers and public cloud vendors.

What we're looking for

• 7+ years of experience in ML infrastructure, systems architecture, or efficiency roles.
• Deep understanding of foundation model inference/serving at scale and distributed training techniques.
• Proven ability to drive complex cross-functional technical initiatives through influence.
• Strong analytical skills for designing utilization analyses and capacity models.
• Expertise in GPU/TPU hardware, cluster scheduling, and memory hierarchies.
• Clear communication skills for presenting to senior leadership and collaborating with ML engineers.