Senior Machine Learning Engineer, Wallet, Payment & Commerce
Apple
7 days ago
On-site
Austin, Texas, United States
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? \\n\\nIf so, we invite you to come and join the Apple Wallet, Payment \u0026 Commerce team in transforming the smartphone into a device that secures the user"s digital life without sacrificing privacy!\\n
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.
Prevent fraud, improve security, and drive operational efficiency across company platforms including Apple Wallet by designing and delivering end-to-end machine learning and data science solutions, leveraging classification, anomaly detection, behavioral modeling, and feature engineering techniques.\\nPartner with software engineers, security engineers, program managers, and business stakeholders to define problems, develop data-driven solutions, implement execution plans, and communicate results on a regular cadence to both technical and non-technical audiences.\\nOwn the full ML lifecycle for assigned problem domains; translating business and customer needs into production systems through feature engineering, model implementation, training, evaluation, and performance reporting, applying deep expertise in machine learning and data science to deliver innovative, production-quality solutions in an agile environment.\\nLead project planning and end-to-end program management of data collection initiatives for Wallet machine learning programs, including requirements gathering, scope definition, prioritization, vendor coordination, resource allocation, and scheduling of deliverables; collaborate with privacy, legal, and research partners to ensure data collection practices comply with regulatory, ethical, and informed consent requirements, including IRB processes where applicable.\\nDrive improvements to data operations across supported ML features; increasing dataset diversity and quality while reducing acquisition lead time and cost through scalable workflows that combine human annotation pipelines and automated machine computation, incorporating advances in ML modeling approaches.\\nDocument and share technical knowledge of partner systems, risk features, fraud modeling approaches, and decision system performance with internal team members to build shared understanding and enable cross-functional collaboration.
Master"s degree in Computer Science, Statistics, Machine Learning, or equivalent field (e.g., Business Analytics with quantitative focus).\\nAt least five years of industry experience deploying machine learning algorithms — including classification, clustering, and anomaly detection — to support customer-facing features in production environments.\\nDeep expertise working with relational databases and SQL, and large-scale distributed computing systems such as Hadoop and Spark.\\nStrong 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.\\nExperience 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 \u0026 Biases), and ML model development frameworks (e.g., Turi Create).\\nExperience implementing privacy-preserving techniques on production data pipelines and ML models across multiple projects.\\nExperience 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.\\nDomain expertise in fraud detection, risk modeling, or security-focused machine learning applications.
Experience with the secure handling, processing, and governance of sensitive personal data in production ML systems.\\nExperience integrating device-based signals and features into risk models, including identification of device-based fraud risk indicators.\\nPrior experience with Institutional Review Board (IRB) processes, informed consent frameworks, and the design and execution of user studies for data collection purposes.\\nDemonstrated history of measurable business impact through fraud prevention with minimal disruption to the legitimate customer experience.\\nFamiliarity with internal datasets, tooling, and systems relevant to payments, Wallet, and fraud decisioning.
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.
Prevent fraud, improve security, and drive operational efficiency across company platforms including Apple Wallet by designing and delivering end-to-end machine learning and data science solutions, leveraging classification, anomaly detection, behavioral modeling, and feature engineering techniques.\\nPartner with software engineers, security engineers, program managers, and business stakeholders to define problems, develop data-driven solutions, implement execution plans, and communicate results on a regular cadence to both technical and non-technical audiences.\\nOwn the full ML lifecycle for assigned problem domains; translating business and customer needs into production systems through feature engineering, model implementation, training, evaluation, and performance reporting, applying deep expertise in machine learning and data science to deliver innovative, production-quality solutions in an agile environment.\\nLead project planning and end-to-end program management of data collection initiatives for Wallet machine learning programs, including requirements gathering, scope definition, prioritization, vendor coordination, resource allocation, and scheduling of deliverables; collaborate with privacy, legal, and research partners to ensure data collection practices comply with regulatory, ethical, and informed consent requirements, including IRB processes where applicable.\\nDrive improvements to data operations across supported ML features; increasing dataset diversity and quality while reducing acquisition lead time and cost through scalable workflows that combine human annotation pipelines and automated machine computation, incorporating advances in ML modeling approaches.\\nDocument and share technical knowledge of partner systems, risk features, fraud modeling approaches, and decision system performance with internal team members to build shared understanding and enable cross-functional collaboration.
Master"s degree in Computer Science, Statistics, Machine Learning, or equivalent field (e.g., Business Analytics with quantitative focus).\\nAt least five years of industry experience deploying machine learning algorithms — including classification, clustering, and anomaly detection — to support customer-facing features in production environments.\\nDeep expertise working with relational databases and SQL, and large-scale distributed computing systems such as Hadoop and Spark.\\nStrong 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.\\nExperience 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 \u0026 Biases), and ML model development frameworks (e.g., Turi Create).\\nExperience implementing privacy-preserving techniques on production data pipelines and ML models across multiple projects.\\nExperience 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.\\nDomain expertise in fraud detection, risk modeling, or security-focused machine learning applications.
Experience with the secure handling, processing, and governance of sensitive personal data in production ML systems.\\nExperience integrating device-based signals and features into risk models, including identification of device-based fraud risk indicators.\\nPrior experience with Institutional Review Board (IRB) processes, informed consent frameworks, and the design and execution of user studies for data collection purposes.\\nDemonstrated history of measurable business impact through fraud prevention with minimal disruption to the legitimate customer experience.\\nFamiliarity with internal datasets, tooling, and systems relevant to payments, Wallet, and fraud decisioning.