Are you motivated by providing software security technologies to help users protect their accounts and provide the best customer experience? Are you a Machine Learning Engineer who enjoys crafting, implementing and operating analytical solutions?
If so, we invite you to come and join the Apple Wallet, Payment & Commerce team in transforming the smartphone into a device that secures the user's digital life without sacrificing privacy!
Description
Our team employs predictive modeling and statistical analysis techniques and builds end-to-end solutions for improving security, fraud prevention, and operational efficiency across Apple. Our team collaborates cross-functionally with engineering teams across the company. Apple's dedication to customer privacy, the adversarial nature of fraud, and the enormous scale of the business present exciting challenges to traditional machine learning and data science techniques.
Minimum Qualifications
Master's degree in Computer Science, Statistics, Machine Learning, or equivalent field (e.g., Business Analytics with quantitative focus).
At least five years of industry experience deploying machine learning algorithms — including classification, clustering, and anomaly detection — to support customer-facing features in production environments.
Deep expertise working with relational databases and SQL, and large-scale distributed computing systems such as Hadoop and Spark.
Strong programming skills in one or more of the following languages: Python, Scala, or Java; familiarity with Objective-C or Swift for on-device model deployment contexts.
Experience with ML workflow and data management tooling, including workflow orchestration frameworks (e.g., Airflow), distributed compute frameworks (e.g., Ray), experiment tracking platforms (e.g., Weights & Biases), and ML model development frameworks (e.g., Turi Create).
Experience implementing privacy-preserving techniques on production data pipelines and ML models across multiple projects.
Experience in data acquisition program management, including working with external vendors and procurement teams, and designing and executing user studies to build high-quality labeled datasets.
Domain expertise in fraud detection, risk modeling, or security-focused machine learning applications.
Preferred Qualifications
Experience with the secure handling, processing, and governance of sensitive personal data in production ML systems.
Experience integrating device-based signals and features into risk models, including identification of device-based fraud risk indicators.
Prior experience with Institutional Review Board (IRB) processes, informed consent frameworks, and the design and execution of user studies for data collection purposes.
Demonstrated history of measurable business impact through fraud prevention with minimal disruption to the legitimate customer experience.
Familiarity with internal datasets, tooling, and systems relevant to payments, Wallet, and fraud decisioning.
