Camera Machine Learning Engineer

Are you a passionate Machine Learning Engineer with a deep love for photography? Join Apple's Camera Hardware Engineering team and help us redefine the camera experience for millions of users worldwide. As a key player in our innovative team, you will collaborate closely with hardware, software, and image processing specialists to develop cutting-edge camera technologies.

As a Machine Learning Engineer in the Camera Hardware Engineering group you will be responsible for all research, design, development, test, and qualification of camera hardware for Apple products. This team is seeking an experienced Machine Learning Engineer with a background in Camera and image sensor technologies. You will bring your expertise to the team and be responsible for ongoing design, evaluation, benchmarking and characterization of Apple camera products.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Electrical Engineering, Physics, Optics, or related field and 10 years of relevant experience
• Experience with Python programming and deep learning frameworks like PyTorch
• Experience with machine learning and computer vision principles, and algorithms.
• Experience with camera and/or image sensor technologies.

Preferred Qualifications

• MS or Ph.D. in Machine Learning, EE, CS, Physics, Optics, or equivalent and 3+ years of experience in machine learning research or relevant industry experience
• Experience with applying deep learning to various computer vision tasks, such as: Object recognition, Image segmentation, Inpainting, and Anomaly detection
• Experience in applying generative AI and reinforcement learning techniques to enhance hardware design processes.
• Experience with using advanced ML methods to optimize system architectures and improve image quality in the hardware design domain.
• Experience with image quality metrics and evaluation methodology
• Experience with camera components, functions, and the camera ISP pipeline
• Experience with megapixel CMOS image sensor technology, lens selection, and qualification