Job description

We are seeking an experienced engineer to provide privacy guidance to teams across Apple working on machine learning and generative Al intrastructure. This includes feature reviews and the design of new, privacy preserving data collection methodologies to enable the evaluation of Al systems. It also includes guiding the roadmap of privacy technologies at Apple like differential privacy and private federated learning. In this role you will: -Review features to identify privacy exposures and partner with teams to design mitigations. -Audit new products to identify bugs in development, and review customer data collected by engineering teams to drive decisions for privacy impact. -Communicate privacy risks and potential mitigations to senior leadership to drive decisions. -Guide the development of data collection systems that enable training and evaluation of generative Al systems while preserving privacy. -Partner with cryptographers and technical experts in SWE and AIML to develop our roadmap for privacy technologies, and provide guidance to engineers and leaders on the right privacy technology to use when developing new features. Successful candidates will need to have superb communication skills and a passion for protecting privacy. Creative problem solving, analytical, and deductive reasoning skills are critical for this position. You will be working on multiple unrelated systems each week, so the ability to learn quickly and an excitement for new things is a must. BS in Computer Science or equivalent experience Programming experience Ability to analyze system architectures for privacy impact Ability to learn and research new technologies rapidly, assess privacy exposures, and suggest mitigations Ability to analyze system architectures for privacy impact Ability to solve complex problems independently Strong collaboration, communication, interpersonal, and organizational skills Passion for customer privacy Demonstrated interdisciplinary interest in the intersection of technology, policy, and society Experience with foundation models, including training and evaluation Experience with differential privacy or private federated learning

Requirements

Must-haves

-Review features to identify privacy exposures and partner with teams to design mitigations.
Audit new products to identify bugs in development, and review customer data collected by engineering teams to drive decisions for privacy impact.
-Communicate privacy risks and potential mitigations to senior leadership to drive decisions.
-Guide the development of data collection systems that enable training and evaluation of generative Al systems while preserving privacy.
BS in Computer Science or equivalent experience
Programming experience
Ability to analyze system architectures for privacy impact
Ability to learn and research new technologies rapidly, assess privacy exposures, and suggest mitigations
Ability to analyze system architectures for privacy impact
Ability to solve complex problems independently
Strong collaboration, communication, interpersonal, and organizational skills
Passion for customer privacy
Demonstrated interdisciplinary interest in the intersection of technology, policy, and society
Experience with foundation models, including training and evaluation
Experience with differential privacy or private federated learning

Nice-to-haves

None flagged.

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Keywords to mirror

machine learning

Platform: apple
Effort: medium

Benefits

Benefits detected in the listing.

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