Machine Learning Engineer
Summary
iPhone is the most popular camera in the world, with billions of photos taken every year. The seamless hardware-software integration consistently pushes mobile photography boundaries. Features like the Photonic Engine, Portrait mode, and Super-high-resolution photos transform moments into magical experiences that delight our customers globally.
The Camera Technologies & Systems team, within Camera & Photos org, delivers unparalleled image and video experiences by innovating at the intersection of state-of-the-art machine learning and computer vision. As a Machine Learning Engineer, you’ll pioneer research and develop groundbreaking generative AI for image enhancement and restoration. Your work will enable new camera capabilities across the Apple ecosystem. Every "Shot on iPhone" billboard showcases our ingenuity; we invite you to join our mission and contribute to a world-renowned visual narrative.
Description
As a Machine Learning Engineer, you will be instrumental in driving the innovation of Apple's next-generation iPhone and iPad camera algorithms. You will identify, research, and develop cutting-edge generative AI-based machine learning models to address complex challenges in camera applications. You will collaborate cross-functionally with GPU optimization and framework teams to seamlessly integrate and deploy these advanced features into production, directly shaping the future of mobile imaging.
Minimum Qualifications
- Generative AI Expertise: Strong understanding and practical experience with advanced generative AI architectures, including (but not limited to) diffusion models, Generative Adversarial Networks (GANs), and autoregressive models.
- Technical Proficiency: Strong programming skills in Python and deep learning frameworks (e.g., PyTorch).
- An MS or PhD in Computer Science, Engineering, or a related technical discipline.
Preferred Qualifications
- Advanced Generative AI Applications: practical experience in the design, training, and fine-tuning of complex generative AI architectures, encompassing diffusion models, autoregressive models, GANs, and VLMs.
- Computer Vision Foundation: Robust theoretical and practical foundation in core computer vision principles, particularly applied to image and video analysis and processing.
- Analytical & Communication Skills: Exceptional analytical problem-solving capabilities and technical communication skills, essential for cross-functional collaboration and presenting complex research.
- Proven Research: Publication record in highly selective, peer-reviewed conferences (e.g., CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, ICML, ICLR)