The Camera Imaging Algorithms team is seeking passionate machine learning engineers with expertise in computational photography and computer vision. This role involves innovating and collaborating with cross-functional teams to enhance Apple's camera technology. The engineer will focus on developing novel machine learning techniques for image restoration and fusion applications. Candidates should have a strong background in deep learning and classical computer vision techniques.

• Strong coding skills in Python, with experience in C/C++ being a significant plus.
• Demonstrated ability in developing machine learning algorithms for computational photography and computer vision.
• Extensive knowledge of camera processing pipelines and classical computational photography techniques.
• Experience with deep learning techniques for image restoration and fusion applications, along with familiarity with frameworks like PyTorch or TensorFlow.
• MS or PhD in Computer Vision, Machine Learning, Computer Science, Electrical Engineering, or related fields.

• Contribute to algorithm design and implementation for camera imaging.
• Develop and train deep learning models for various imaging applications.
• Collaborate with teams focused on camera hardware, firmware, GPU, and image quality.
• Leverage knowledge of classical computer vision to enhance the imaging pipeline.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.