machine learning platform engineer, Evaluation & Privacy

2+ years of industry experience with a Bachelor’s degree or equivalent experience in Computer Science or a related technical field.\nProficiency in one or more programming languages such as Python and Swift.\nExperience building software that runs in the background or on a schedule on largely autonomous systems like iPhone - not as a self-contained app.\nExperience designing and operating distributed systems at scale.\nStrong problem-solving skills, creativity in finding effective solutions, and the ability to collaborate with teams across the company.

Passion for customer privacy.\nExperience with privacy-enhancing technologies such as differential privacy, secure multi-party computation, private federated learning, or PII redaction frameworks.\nExperience training, evaluating, and deploying machine learning models to production.\n5+ years of industry experience.\nMaster’s degree or PhD or equivalent experience in Computer Science, Software Engineering, or a related technical field.

Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there’s no telling what you could accomplish. Apple delivers great features and great privacy to our users. The Privacy Preserving Machine Learning team works with teams all across the company to provide tools and support for state of the art privacy-preserving measurement and machine learning. Join us and help build amazing products while our commitment to privacy.

In this role you will work with engineers and scientists across many Apple products to bring their machine learning investigations onto our platform for privacy-preserving machine learning. You will partner with them to understand the goals of each feature and the data required to improve the experience, then design the systems that turn those investigations into production capabilities. The results of this work will be better features deployed to billions of devices, all while protecting the privacy of every person using them.