Apple AR/VR Job | visionOS Performance Engineer
Job（岗位）: visionOS Performance Engineer
Citys（岗位城市）: San Francisco Bay Area, California, United States
Date（发布日期）: 2025-10-22
Summary（岗位介绍）
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something.
The Vision Product Group at Apple is working on exciting new technologies. We are looking for a driven and dedicated performance engineer. This team is building the core foundational platform for some of Apple's most advanced technologies in spatial computing. As part of our creative organization, you will have a uniquely rewarding opportunity to craft future products that will delight and inspire millions of people every day.
Qualifications（岗位要求）
Minimum BS and 1+ years of relevant industry experience
Strong programming ability in Python, C/C++ or Swift
Strong written and verbal communication skills
Understanding of Computer Architecture, Operating Systems, Threading models and performance controllers
Experience with optimizing inference latency, memory and compute.
Description（岗位职责）
As an engineer in this role you will help with ANE efficiency as well as improving overall visionOS responsiveness. You will work closely with Computer Vision and Apple Intelligence teams to diagnose performance bottlenecks and develop innovative solutions to optimize compute and memory footprint.
Additional Requirements（额外要求）
Proficiency with PyTorch, Tensorflow or CoreML
Knowledge of AI/ML fundamentals including model architecture analysis, ML inference performance optimization for a given hardware architecture.
Familiarity with optimizing model architectures for on-device inference is a big plus.
Familiarity with Computer Vision, Transformers and LLM architectures.
