Senior Machine Learning Systems Infrastructure Engineer - SIML, ISE

Do you think Computer Vision and Machine Learning can change the world? Do you think it can transform the way millions of people collect, discover and share the most special moments of their lives? We truly believe it can. We are the System Intelligent and Machine Learning (SIML) group that provides foundational computer vision and machine learning technologies to Apple’s ecosystem. Our work is behind essential features such as Camera, Text & Handwriting recognition, and Apple Intelligence experiences (Image Playground, Writing Tools, Smart Script, Math Notes..). This is an exciting time to join us: grow fast, and have an impact on multiple key features on your first day at Apple! In this role, you will be building infrastructure to support product-focused machine learning projects. You will build systems that leverage machine learning to index terabytes of data for projects in domains like image generation, LLMs, computer vision, natural language processing, human-computer interaction and text recognition. You will define and build out systems for analysis of failure modes of algorithms built upon this data, and for reporting overall benchmarking results for model comparisons. The technology you build will play a major role in defining important datasets, ingestion of annotated/inferred data into our systems, and making the data available to machine learning scientists in a seamless manner. This role requires a diverse set of skills, from tackling low-level distributed computing challenges at bare metal, to contributing to internal client user experiences by building stable interfaces, and everything in between. As the ideal candidate, you enjoy variety, and are passionate about working on new things on a frequent basis. You love working with a large number of people across teams throughout the company, and driving solutions that take multiple partners into account. You are either a machine learning industry pro, or an outstanding engineer wanting to expand your horizons and learn more about the nitty gritty of how ML projects are launched from inception to release.