ML Platform Engineer

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. You 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. Working 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 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 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 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)

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