The Machine Learning Platform Engineer role in the VCV group focuses on building and maintaining the infrastructure that enables machine learning at scale across Apple's products. The team is dedicated to pioneering human-centric real-time features and delivering high-quality experiences. Engineers will work on MLOps platforms, cloud and on-device deployment systems, and data engineering platforms. This position requires collaboration with ML algorithms engineers, data scientists, and quality assurance teams to deploy advanced computer vision technologies on Apple devices.

• Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
• 2+ years of relevant industry experience in software engineering, machine learning infrastructure, or related fields.
• Strong programming skills in Python, C++, and/or Swift.
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX.
• Knowledge of the machine learning model development lifecycle, including data preprocessing, model training, evaluation, and deployment.
• Experience with distributed systems, cloud computing, or large-scale data processing.
• Strong foundational knowledge in Computer Science and software engineering principles.
• Master's degree in Computer Science, Machine Learning, or a related technical field.
• 2+ years of experience in ML infrastructure, platform engineering, or production ML systems.
• Experience with Apple's frameworks including CoreFoundation, RealityKit, and CoreML.
• Hands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code.
• Experience with containerization technologies (Docker, Kubernetes) and orchestration systems.
• Knowledge of cloud platforms (AWS, GCP, Azure) and distributed computing frameworks (Spark, Ray, etc.).
• Experience with GPU programming and hardware acceleration (Metal, CUDA, OpenCL).

• Build and maintain scalable machine learning infrastructure for training, evaluation, and deployment of computer vision and multimodal models.
• Develop MLOps platforms and tools that streamline the ML development lifecycle from data ingestion to model deployment.
• Create robust data pipelines for large-scale data collection, curation, preprocessing, and management.
• Implement on-device ML integration systems that deploy state-of-the-art algorithms to Apple devices.
• Collaborate closely with ML algorithms engineers, data scientists, and quality assurance teams to deploy advanced computer vision technologies.

• 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.