Architect, Foundation Model Compute Infra & Scale

Apple, Inc.; Seattle (WA); On-site; USD 205,000 - 374,000; Full time

Benefits offered by this job

Stock options

Relocation assistance

Comprehensive medical and dental

Job summary

Apple in Seattle is hiring a Senior Staff/Principal Engineer for the Foundation Model Compute Infrastructure team to architect, build, and operate large-scale AI training and inference systems.

You will lead distributed scheduling, cluster management, and accelerator provisioning, collaborating with researchers and hardware partners to advance TPU-based workloads while delivering reliable, scalable tools for researchers and engineers.

Qualifications

• 8+ years building distributed systems or large-scale infrastructure.
• Strong hands-on experience with Kubernetes, cloud infra and production systems.
• Proficiency in Python, Go, C++, or similar languages used for infra.

Responsibilities

• Lead by example with hands-on coding to solve architecture and implementation challenges.
• Design and build distributed scheduling, quota, and capacity management systems to maximize accelerator utilization.
• Improve fault tolerance and recovery for large-scale training/inference workloads across thousands of accelerators.
• Architect and operate Kubernetes-based infrastructure including provisioning, node lifecycle, and autoscaling.
• Collaborate with cloud and hardware partners to qualify and deploy new TPU generations.
• Build self-service tools, APIs, and observability for job submission, monitoring, and profiling.
• Develop scalable services for model fine-tuning and inference.
• Partner with researchers and security teams to translate research needs into production infra.
• Mentor engineers and drive architectural reviews and best practices.

Skills

Kubernetes

Python

Go

C++

Distributed systems leadership

Tools

TPU

GPU fleets

Docker

Job description

Apple in Seattle is hiring a Senior Staff/Principal Engineer for the Foundation Model Compute Infrastructure team to architect, build, and operate large-scale AI training and inference systems.

You will lead distributed scheduling, cluster management, and accelerator provisioning, collaborating with researchers and hardware partners to advance TPU-based workloads while delivering reliable, scalable tools for researchers and engineers.