About the job
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 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.
Responsibilities
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.
Work with the Privacy Preserving Measurement and ML team to design, develop and deploy end to end measurement systems with high utility that meet Apple’s industry-leading privacy bar.
Work with the Privacy Engineering team to communicate system design tradeoffs, privacy risks and potential mitigations to senior leadership to drive decisions.
Build cross-team consensus and collaboration.
Qualifications
Minimum
Passion for customer privacy.
Strong collaboration, communication, interpersonal, and organizational skills.
Strong software engineering skills and ability to solve complex problems independently.
Experience with differential privacy or private federated learning.
BS in Computer Science, EE or equivalent experience.
Preferred
Real-world experience implementing privacy/trust/security measures which have shipped in a consumer product and/or service.
Participation in public standards forums or academic publications in privacy and machine learning strongly preferred.
Ability to analyze systems’ architectures for privacy impact.
Ability to learn and research new technologies and use-cases rapidly, assess privacy exposures, and suggest mitigations.