AIML Privacy-Engineering Rotation

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 theres no telling what you could accomplish. Apple delivers great features and privacy to our users. This unique role combines Privacy Engineering with hands-on software engineering work with the Privacy Preserving Machine Learning team. 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. We are looking for an outstanding candidate with a strong interest in privacy in practice to take on this unique role.

We are seeking an experienced engineer and privacy advocate to build privacy-preserving technologies for measurement and machine learning and guide policy and product decisions to uphold Apples industry leading privacy candidates will need to have strong interpersonal skills and the ability to influence and build consensus and work across multiple teams and organizations on a regular basis.

Experience a unique role with rotations on two teams to experience hands-on software engineering and privacy engineering. At the end of the rotation period you will have the opportunity to continue in either of the teams full-time. nWork with the Privacy Preserving Measurement and ML team to design develop and deploy end to end measurement systems with high utility that meet Apples industry-leading privacy with the Privacy Engineering team to communicate system design tradeoffs privacy risks and potential mitigations to senior leadership to drive cross-team consensus and collaboration.

Passion for customer collaboration communication interpersonal and organizational software engineering skills and ability to solve complex problems with differential privacy or private federated in Computer Science EE or equivalent experience.

Real-world experience implementing privacy/trust/security measures which have shipped in a consumer product and/or in public standards forums or academic publications in privacy and machine learning strongly preferred. nAbility to analyze systems architectures for privacy to learn and research new technologies and use-cases rapidly assess privacy exposures and suggest mitigations.