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 Apple's 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 use. The 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 Apple's 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.

DESCRIPTION

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 partners. Our 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 environment. If you're

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, we'd love to hear from you.

MINIMUM QUALIFICATIONS

Experience with machine learning methods such as classification, clustering, and

anomaly detection. Strong programming skills in one or more languages such as

Python, Scala, or Java. Experience processing and analyzing data at scale using

distributed data or compute frameworks. Ability to communicate the results of

analysis clearly and succinctly to a range of audiences. Experience delivering

results on ambiguous, loosely defined problems, working with others. Rigorous

analytical thinking, including the ability to question assumptions, reason

through a problem, and justify a recommendation with sound evidence.

PREFERRED QUALIFICATIONS

Experience deploying machine learning in resource-constrained or real-time

environments, such as on-device deployment, model compression, or optimizing for

inference budgets. Experience with distributed data and compute frameworks such

as Spark, Ray, or Daft. Familiarity with privacy-preserving machine learning

techniques. Background in fraud detection, risk modeling, or security-focused

machine learning. Familiarity with iOS development. We're open to a range of

specializations and are excited by candidates who bring a differentiating

strength to the team, whether that's a research background, deep systems

thinking, or expertise we don't yet have. Tell us what you'd add.