Machine Learning Systems Engineer – Video Computer Vision

The incredible potential of multimodal foundation models and large language models has unlocked machine learning applications that were previously thought infeasible. The Video Computer Vision (VCV) group is looking for a highly motivated and skilled Machine Learning Systems Engineer to help us ship cutting-edge computer vision technology on Apple devices. The VCV organization has pioneered groundbreaking features like FaceID/FaceKit, Gaze/Hand Gesture Control, Body Tracking, and 2D/3D Scene Understanding fundamentally changing how millions of users interact with technology. We seamlessly balance research and product requirements to deliver pioneering, Apple-quality experiences. By innovating across the full stack and partnering closely with hardware, software, and AI teams, we shape future products and bring our architectural vision to life. ## Description As a member of the Video Computer Vision team, you will train, evaluate, and deploy purpose-built vision models on Apple hardware. You will develop innovative techniques to optimize model performance, efficiency, and scalability, ensuring a seamless user experience under strict on-device constraints. ## Responsibilities Develop on-device software that bridges multimodal AI models and computer vision technologies with production systems deployed across Apple devices. Optimize on-device inference latency, memory footprint, and computational efficiency of CV/ML models. Benchmark, profile, and evaluate the power consumption and thermal performance of models running on Apple silicon. ## Minimum qualifications Bachelor’s degree in Computer Science, Machine Learning, or a related discipline, and 3+ years of relevant industry experience. Strong ML fundamentals. A proven track record of writing high-quality production code for shipped CV/ML features. Solid understanding of operating system fundamentals and extensive programming experience in Python and C++. Hands-on experience with PyTorch and familiarity with the end-to-end ML lifecycle (data preprocessing, training, evaluation, and edge deployment). Experience with Supervised Fine-Tuning (SFT) pipelines to adapt vision and multimodal foundation models for specialized, on-device downstream tasks. Robust foundational understanding of machine learning architectures, specifically Multimodal LLMs and the integration of ML components into complex production systems. ## Preferred qualifications Programming experience with Swift and familiarity with CoreML, CoreFoundation, and RealityKit frameworks. Fundamental knowledge of real-time video pipelines, image transformations, and rendering loops. Experience optimizing models for neural network accelerators (e.g., Apple Neural Engine or mobile GPUs).