What are the responsibilities and job description for the Machine Learning Engineer - Computer Vision & Data Systems position at Apple, Inc.?
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.
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.