About the job
As a technical leader within the team, you will own the end-to-end architecture, rapid prototyping, and productization of advanced machine learning-based auto-focus algorithms. You will maneuver through ambiguity independently to drive the long-term ML roadmap for AF, spearheading the design of novel learning-based systems integrated on Apple camera platforms which achieve seamless auto-focus user experience in any scene condition in both bright and low light.
Responsibilities
Provide meaningful and significant contributions to the company's objectives by helping set the technical direction for next-generation multi-modal hybrid auto-focus algorithms.
Architect solutions that leverage advanced machine learning, the Apple Neural Engine (ANE), Apple-designed Image Signal Processor (ISP) pipelines, TrueDepth Camera, LiDAR Scanner, and other specialized hardware to redefine how camera auto-focus works at a fundamental level.
Act as a key technical pillar, creating partnerships across Silicon Design, Camera Hardware/Software, and Quality Assurance towards the creation of novel solutions in the space of real-time, machine learning based auto-focus capture solutions applicable across all of Apple’s products.
Qualifications
Minimum
MS in Computer Science, Machine Learning, Electrical Engineering, or a related field.
Experience in defining datasets for machine learning network training on low-level vision tasks as well as dataset curation and data augmentation strategies for robust training.
Expertise in modern machine learning (ML) frameworks and libraries, specifically PyTorch or TensorFlow/TFLite/LiteRT.
Strong software engineering and architectural skills, highly skilled at coding in Python and C.
Preferred
Experience with machine learning for practical low level computer vision applications including one or more of the following areas: auto-focus, stereo disparity/depth, depth estimation, defocus/blur estimation, optical flow estimation, sensor fusion.
Experience with defining datasets for training temporal networks.
Good knowledge of optics (Point Spread Functions, Depth-of-Field, etc.) and image quality metrics impacting critical image sharpness evaluation (Modulation Transfer Function, Spatial Frequency Response, Acutance, Blur/Defocus Estimation etc).
Track record of pioneering innovation comprising publications in top-tier computer vision conferences (e.g., CVPR, ICCV, ECCV) and/or patents.