Senior AI Infrastructure Architect

Apple Inc.; Seattle (WA); Hybrid; USD 296,000 - 472,000; Full time

Benefits offered by this job

Medical and dental coverage

Retirement benefits

Discounts and services

Tuition reimbursement

Bonuses or commissions

Relocation

Job summary

Apple Inc. in Seattle seeks a Distinguished Engineer to provide technical leadership in building and evolving next-generation AI infrastructure.

You will shape architecture and long-term strategy for large-scale training and inference systems, working at the intersection of AI research, systems, and cloud infrastructure. You will influence how frontier and production models are trained, deployed, and scaled across diverse accelerator platforms, partnering with ML researchers and engineers to

Qualifications

• Bachelor’s degree in Computer Science or related field.
• 15+ years designing and building large-scale distributed systems.
• Proficiency in at least one backend language (Python/C++/Go/Rust).
• Experience with cloud-native architectures and compute orchestration (Kubernetes).
• Hands-on experience with ML accelerators such as GPUs/TPUs.
• Track record mentoring senior engineers and setting architectural vision.

Responsibilities

• Set technical vision and roadmaps for frontier AI infrastructure across multi-cloud fleet.
• Design and build distributed systems for job scheduling and cluster management.
• Collaborate with ML researchers and engineers to scale training/inference workloads.

Skills

Distributed systems design

Python

C++

Go

Rust

Back-end architectures

Mentoring

Education

Bachelor's in CS

Master/PhD preferred

Tools

Kubernetes

PyTorch

JAX

GPUs/TPUs

Job description

Apple Inc. in Seattle seeks a Distinguished Engineer to provide technical leadership in building and evolving next-generation AI infrastructure.

You will shape architecture and long-term strategy for large-scale training and inference systems, working at the intersection of AI research, systems, and cloud infrastructure. You will influence how frontier and production models are trained, deployed, and scaled across diverse accelerator platforms, partnering with ML researchers and engineers to