Job Details

Role Number: 200619215-3760

Summary

Scaling machine learning workloads across thousands of GPUs and TPUs creates challenges that few engineers ever encounter. In Apple’s Machine Learning Platform Technologies organization, we build the infrastructure that powers large-scale ML training and inference workloads, bringing together expertise in distributed systems, machine learning infrastructure, and high-performance computing.

Description

As a performance engineer in the ML Compute Efficiency team, you’ll tackle ambiguous systems challenges, identify inefficiencies and build solutions that maximize accelerator utilization, reduce idle and fragmented capacity, and minimize recovery periods. This includes analyzing accelerator performance, digging into various parallelism techniques, and refining workload scheduling and orchestration across the compute fleet.

Minimum Qualifications

• Experience with large-scale distributed systems for AI/ML workloads running on GPUs or TPUs.

• Strong software engineering skills with experience developing and optimizing training frameworks (e.g. PyTorch, JAX) using C/C++ or Python.

• Experience working on cross-functional projects with ML research and infrastructure teams.

• Familiarity with model architectures and various training techniques.

• Bachelor’s degree in Computer Science or equivalent experience, with 7+ years of industry experience.

Preferred Qualifications

• Have a track record of delivering transformative performance improvements on large scale infrastructure.

• Ability to analyze ambiguous, distributed systems problems and articulate both high-level strategic metrics and underlying technical complexity.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) .