visionOS Performance Engineer

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.

• Analyze, benchmark and identify performance bottlenecks for ML/Foundation models in Vision Pro
• Develop in-house performance solutions (such as analysis tools) to optimize inference stack & identify performance wins.
• Conduct deep-dive analysis to identify & reduce bottlenecks and measure inference improvements in terms of memory, compute and latency.
• Partner with feature and product teams to consult on algorithm and SW architectural design decisions to achieve visionOS performance goals
• Optimize Software and Hardware via tunable parameters & integrate research findings into product implementation

• 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.

• 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.