Apple's machine learning features are only as good as the data behind them, and our team builds the platform that lets teams create data that fills the gaps real-world collection can't, provides privacy-preserving ways to train and evaluate our models safely, and helps us ensure our products have seen diverse inputs to generalize properly across the many parts of the world where we operate.
In the AI & Machine Learning (AIML) organization, our team builds the self-service tools and platform that turn ideas, text, and images into large-scale generated datasets. We don't produce the datasets ourselves: ML engineers, data scientists, designers, feature teams, ML data operations teams, and Safety and Responsible AI teams across Apple use our platform to generate their own. That data is only valuable if it matches the real world closely enough to train on and to evaluate against, proving our models and features meet Apple's quality bar before they reach customers. As a software engineer on the platform, you'll sit between that technology and the people who depend on it: understanding what they want to create, building the features that make it possible, and helping them get the most from generative AI. It's a hands-on engineering role for someone who enjoys the people side as much as the code.
