Machine Learning Engineer – Computer Vision & Data Systems

At Apple we are dedicated to creating technologies that enrich peoples 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 are seeking a Machine Learning Engineer with strong expertise in computer vision and large-scale data 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:nn- Design build and maintain large-scale data processing workflows ensuring efficiency scalability and reliability across diverse data sources and - Develop and optimize computer vision models that power core product experiences including areas such as image understanding multi-view geometry 3D reconstruction and visual - 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 - 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 learningbased vision models and at least one area such as detection segmentation 3D vision geometric methods tracking or self-supervised -on experience developing machine learning models using frameworks such as PyTorch or 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 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 designing or evaluating agentic systems including LLM-powered tools RAG pipelines or automated data reasoning with prompt engineering tool-use patterns and LLM model deploying ML models at scale including monitoring evaluation and continuous of data quality assessment dataset curation methodologies and evaluation with GPU-based optimization large-batch training or distributed multi-functional collaboration skills and the ability to lead technical initiatives.