Apple's Strategic Data Solutions (SDS) team is seeking a dedicated individual to implement and operate analytical solutions that directly impact Apple and its customers. As a Computer Vision Machine Learning Engineer (CV MLE), you will develop end-to-end solutions to prevent fraud while enhancing the customer experience. The role involves working with diverse data sources, curating datasets, building computer vision pipelines, and deploying analytics in real-time platforms. You will face exciting challenges in combating fraud through innovative data science approaches.

• M.S. or Ph.D. in Computer Science, Machine Learning, Engineering, or a related technical field.
• At least 2 years of proven experience in computer vision or machine learning.
• Practical experience and theoretical understanding of computer vision methods or deep learning models.
• Ability to code and debug in a general programming language such as Python or Java.
• Ability to use a querying language such as SQL to extract insights from data.

• Translate business needs into technical solutions.
• Explore tabular and image datasets to identify patterns of fraudulent behavior.
• Develop and deploy updated models to combat adversarial attacks.
• Analyze data and communicate findings to audiences with various technical backgrounds.
• Coordinate with engineers and system administrators to deploy analytics to a real-time decisioning platform.
• Identify and implement new data science tools to enhance team capabilities.

• 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.