Software Performance Engineer, Vision Products Group

The Vision Product Group 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.

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 Foundation Model teams to diagnose performance bottlenecks and develop innovative solutions to optimize compute and memory footprint. Your responsibilities will include:\n\nAnalyze, benchmark and identify performance bottlenecks for ML/Foundation models in Vision Pro\nDevelop in-house performance solutions (such as analysis tools) to optimize inference stack & identify performance wins.\nConduct deep-dive analysis to identify & reduce bottlenecks and measure inference improvements in terms of memory, compute and latency.\nPartner with feature and product teams to consult on algorithm and SW architectural design decisions to achieve visionOS performance goals\nOptimize SW and HW via tunable parameters & integrate research findings into product implementation

Minimum BS and 1+ years of relevant industry experience\nFamiliarity with Operating Systems, embedding programming, parallel programming.\nAcademic or industry experience using C/C++, Python or Swift\nUnderstanding of compilers, runtimes, system performance, system software engineering and ML architectures.\nFamiliarity with optimizing inference latency, memory and compute.\nFamiliarity with Gen AI coding tools. Demonstrated ability to use them for design, code generation, and debugging.

Familiarity with PyTorch, Tensorflow or CoreML\nKnowledge of AI/ML fundamentals including model evaluation and deployment/inference.\nFamiliarity with optimizing model architectures for on-device inference\nFamiliarity with Computer Vision, Transformers and LLM architectures.\nExceptional written and verbal communication skills