The Applied Machine Learning team at Apple is focused on accelerating digital transformation through machine learning across the enterprise ecosystem. They build and operate various platforms and services related to machine learning, GenAI, and big data. The team is seeking a talented engineer who is passionate about building and operating large-scale distributed systems using cutting-edge technologies. The role involves working on projects that enhance multi-tenant systems and optimize high-throughput services.

• Bachelor's Degree in Computer Science, Computer Engineering, or equivalent technical degree
• Proficient programming knowledge in Python, Java, or Go
• Good foundation of Operating Systems, Networking, and Security Principles
• Relevant internship experience
• Excellent analytical and problem-solving skills
• Exposure to Model Training or Fine Tuning methodologies
• Exposure to modern cloud-native big data technologies like Spark, Flink, Trino, or Iceberg
• Exposure to Kubernetes and other cloud-native technologies like Flux or Argo CD

• Build, enhance, and maintain multi-tenant systems employing diverse technologies
• Collaborate with multi-functional teams to deliver impactful customer features
• Lead projects through the full lifecycle, from design discussions to release delivery
• Operate, scale, and optimize high-throughput and highly concurrent services
• Diagnose, resolve, and prevent production and operational challenges

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.