Machine Learning Engineer - Computer Vision & Data Systems

At Apple, we are dedicated to creating technologies that enrich people's lives. Our teams develop products and experiences that empower millions of users globally, by combining world-class engineering with a deep commitment to innovation, quality, and privacy.\\n\\nWe are seeking a Machine Learning Engineer with strong expertise in computer vision and large-scale data processing. In this role, you will contribute to the development of next-generation real-time sensing and data intelligence systems by designing algorithms, building scalable data pipelines, and collaborating with multi-functional teams to deliver high-impact, production-quality solutions.

As a Machine Learning Engineer, you will:\n\n- Design, build, and maintain large-scale data processing workflows, ensuring efficiency, scalability, and reliability across diverse data sources and modalities.\n\n- Develop and optimize computer vision models that power core product experiences, including areas such as image understanding, multi-view geometry, 3D reconstruction, and visual recognition.\n\n- Partner closely with engineering, research, and data teams to translate product requirements into technical solutions. This includes prototyping models, running large-scale experiments, improving data quality, and ensuring seamless integration of algorithms into production systems.\n\n- Explore emerging areas such as LLM-based agents, retrieval-augmented systems, and tool-oriented reasoning to improve internal workflows or data operations.

Strong foundation in computer vision, including experience with deep learning-based vision models and at least one area such as detection, segmentation, 3D vision, geometric methods, tracking, or self-supervised learning.\nHands-on experience developing machine learning models using frameworks such as PyTorch or TensorFlow.\nExperience building or optimizing large-scale data pipelines (e.g., distributed ETL, dataset generation, annotation workflows, data validation, or high-throughput processing).\nProficiency in Python or C++ for algorithm development and data processing.\nExperience working with distributed computing frameworks (e.g., Spark, Ray, or equivalent).

PhD in a relevant field with research directly related to computer vision, large-scale data systems, or multimodal learning.\nExperience designing or evaluating agentic systems, including LLM-powered tools, RAG pipelines, or automated data reasoning workflows.\nFamiliarity with prompt engineering, tool-use patterns, and LLM model behavior.\nExperience deploying ML models at scale, including monitoring, evaluation, and continuous improvement.\nKnowledge of data quality assessment, dataset curation methodologies, and evaluation frameworks.\nExperience with GPU-based optimization, large-batch training, or distributed training.\nStrong multi-functional collaboration skills and the ability to lead technical initiatives.