Machine Learning Platform Engineers

We're starting to see the incredible potential of multimodal foundation and large language models, and many applications in the computer vision and machine learning domain that previously appeared infeasible are now within reach. We are looking for highly motivated and skilled Machine Learning Platform Engineers to join our team in the VCV group and help us enable that potential for realtime human understanding on Apple devices.\n\nThe VCV org has pioneered human-centric real-time features such as FaceID, FaceKit, and Gaze and Hand gesture control which have changed the way millions of users interact with their devices. We balance research and product requirements to deliver Apple quality, pioneering experiences, innovating through the full stack, and partnering with HW, SW and AI teams to shape Apple's products and bring our vision to life.\n\nJoin us to build the infrastructure, MLOps platforms, and deployment systems that power Apple's next generation of intelligent products and experiences.

As part of the VCV team, you will build and maintain the critical infrastructure that enables machine learning at scale across Apple's products. You will work on infrastructure, MLOps, cloud and on-device deployment systems, and data engineering platforms that support our ML development lifecycle.\n\nYou will be responsible for building and maintaining scalable machine learning infrastructure for training, evaluation, and deployment of computer vision and multimodal models. You will 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, and implement on-device ML integration systems that deploy state-of-the-art algorithms to Apple devices.\n\nWorking closely with ML algorithms engineers, data scientists, and quality assurance teams, you'll help deploy state-of-the-art computer vision technologies on Apple devices, balancing performance with the compute and power constraints of on-device inference.

Bachelor's degree in Computer Science, Software Engineering, or related technical field, or equivalent practical experience\n2+ years of relevant industry experience in software engineering, machine learning infrastructure, or related fields\nStrong programming skills in Python, C++, and/or Swift\nExperience with machine learning frameworks such as PyTorch, TensorFlow, or JAX\nKnowledge of machine learning model development lifecycle, including data preprocessing, model training, evaluation, and deployment\nExperience with distributed systems, cloud computing, or large-scale data processing\nStrong foundational knowledge in Computer Science and software engineering principles

Master's degree in Computer Science, Machine Learning, or related technical field\n2+ years of experience in ML infrastructure, platform engineering, or production ML systems\nExperience with Apple's frameworks including CoreFoundation, RealityKit, and CoreML\nHands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code\nExperience with containerization technologies (Docker, Kubernetes) and orchestration systems\nKnowledge of cloud platforms (AWS, Google Cloud Platform, Azure) and distributed computing frameworks (Spark, Ray, etc.)\nExperience with GPU programming and hardware acceleration (Metal, CUDA, OpenCL)