Machine Learning Engineer, Data for Foundation Models

Do you believe that generative models can fundamentally change how people interact with devices? Are you excited by the possibility of connecting people to unprecedented levels of creativity? Come join the team that turned cutting edge Generative AI research into compelling user experiences through Apple Intelligence. We are part of the Intelligent System Experiences team at Apple and work on the core modeling technology behind Genmoji, Image Playground and Photos Generative Edits. From web-scale datasets to distributed training of foundation models, application specific adapters to on-device optimizations, you will have the unique opportunity to advance and ship state-of-the-art technology that impacts Apple users around the world! We are seeking a machine learning research engineer with experience building high-quality datasets for foundation model training. The ideal candidate has solid ML fundamentals, experience with applied research, and hands-on knowledge of constructing large scale and targeted datasets involving image or video to improve the quality of diffusion or auto-regressive foundation models. You will stay at the forefront of the latest AI research to rapidly adopt impactful developments while simultaneously pioneering proprietary and experimental ideas. You will design data annotation, enrichment, curation and synthetic data generation pipelines using classic or genAI tools to improve model quality along specific dimensions aligned with Apple's unique product offerings.M.S. or PhD in Computer Science or a related field such as Electrical Engineering, Robotics, Statistics, Applied Mathematics, or equivalent experience 3+ years of experience improving modern image generation or image understanding models through data ablation experiments Knowledge of ML frameworks such as PyTorch / JAX. Experience leveraging large-scale data frameworks such as Hadoop / Ray. Experience working in an academic or industrial research environment Excellent communication skills, bias-for-action and ability to drive clarity in ambiguous circumstancesHands-on experience improving the quality of diffusion image generation models through training data improvements Track record of strong industrial research output, shipped complex projects or published at top-tier venues