The ML Infrastructure Engineer role focuses on model orchestration and performance within the On-Device Machine Learning team at Apple. This team is dedicated to transforming advanced research into practical applications that enhance machine learning capabilities across Apple devices. The engineer will work closely with model authoring, compiler, and runtime teams to optimize the execution of machine learning models. This position offers the opportunity to contribute to the development of an end-to-end developer experience for machine learning on Apple platforms.

• 3-5 years of experience working on tooling built in Python 3 and C++/Swift.
• Familiarity with common ML model architectures, execution schemes, and operations.
• Experience with PyTorch or related training frameworks.
• Experience working on or adjacent to MLIR-based compilers.
• Familiarity with deploying applications or tooling on Apple platforms.
• Knowledge of programming paradigms for the GPU, CPU, and Neural Engine.
• Experience writing kernels for ML model execution.

• Collaborate with model authoring, compiler, and runtime teams to ensure optimal model execution.
• Develop and maintain the core runtime for machine learning execution across various devices.
• Contribute to the onboarding of cutting-edge architectures to embedded systems.
• Create optimization toolkits for model compression and acceleration.
• Build comprehensive benchmarking and debugging toolchains for machine learning workflows.

• 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.