Machine Learning Engineer, Wallet Intelligence and Machine Learning

Are you motivated to protect users and their accounts while delivering the best possible customer experience Come join the Wallet Intelligence and Machine Learning team where we help secure users digital lives across Apples devices without sacrificing privacy. Machine Learning Engineers here build analytical solutions and think deeply about where they fit into a larger system staying ahead of fraud and applying the best privacy-preserving and fraud-prevention methods available to make Apple products and especially Apple Pay and Apple Wallet the safest platform people can On Device Insights team at Apple develops machine learning models that run directly on users devices to protect them from fraud holding themselves to an exceptionally high bar for privacy. As part of the Wallet Intelligence and Machine Learning team you will help secure users digital lives across Apples devices including Apple Pay and Apple Wallet without sacrificing privacy. This is a mission-driven team that thrives on hard problems healthy skepticism and open collaboration.

We are looking for a Machine Learning Engineer to help develop and launch on-device technologies that keep our users safe working closely with engineering security program management and business work is applied and pragmatic by necessity. Models must run in real time and in the background on the device without slowing down something as simple as an in-app purchase which means designing within real constraints like model size inference budgets and memory. Because we often need to anticipate fraud rather than react to each new pattern as it appears we have to be proactive and think ahead. This role is a chance to take ownership of a problem area build a system-wide understanding of where our models fit and apply your expertise in machine learning in an innovative and fast-moving youre energized by ambiguity motivated by a meaningful mission and the kind of person who digs beneath the surface and questions your own assumptions before forming a recommendation wed love to hear from you.

Take end-to-end responsibility for translating customer and security needs into machine learning solutions from framing the problem through feature engineering model development training evaluation and and deliver models that operate within real-world constraints balancing accuracy against latency model size and on-device compute budgets so that protection never comes at the cost of the user and share a system-wide understanding of where our models fit into the user journey and the fraud-risk journey and use that understanding to anticipate problems rather than react to and advance a high standard for user privacy in everything you across software engineering security program management and business teams to define problems align on solutions and communicate results clearly to both technical and non-technical your thinking openly welcome scrutiny of your own ideas and build trust with the people you work with.

Experience with machine learning methods such as classification clustering and anomaly programming skills in one or more languages such as Python Scala or processing and analyzing data at scale using distributed data or compute to communicate the results of analysis clearly and succinctly to a range of delivering results on ambiguous loosely defined problems working with analytical thinking including the ability to question assumptions reason through a problem and justify a recommendation with sound evidence.

Experience deploying machine learning in resource-constrained or real-time environments such as on-device deployment model compression or optimizing for inference with distributed data and compute frameworks such as Spark Ray or with privacy-preserving machine learning in fraud detection risk modeling or security-focused machine with iOS open to a range of specializations and are excited by candidates who bring a differentiating strength to the team whether thats a research background deep systems thinking or expertise we dont yet have. Tell us what youd add.