Sr./Staff ML Infrastructure Engineer, Compute (TPU Scheduling) - Foundation Model

Summary

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something!

Description

As a Senior/Staff Engineer on the Foundation Model Compute Infrastructure team, you will lead the design and development of scheduling and orchestration systems for large-scale TPU workloads across multi-region clusters.

You will work on distributed systems that manage thousands of accelerators and enable reliable, efficient execution of large-scale training and inference jobs. This role spans scheduling algorithms, cluster lifecycle management, workload orchestration, reliability engineering, and performance optimization.

Preferred Qualifications

Experience building schedulers, resource managers, or orchestration systems for distributed workloads
Experience with accelerator infrastructure such as TPU, GPU
Experience with distributed ML training or inference systems
Familiarity with frameworks such as JAX, PyTorch, TensorFlow, Ray, Pathways
Experience operating large-scale multi-tenant infrastructure in cloud or hybrid environments
Background in performance optimization, fault tolerance, or resource efficiency for large distributed systems
MS or PhD in Computer Science, Engineering, or related field

Minimum Qualifications

7+ years of industry experience building large-scale distributed systems or cloud infrastructure
Strong programming skills in Python, Go, C++, or similar systems languages
Extensive experience with compute infrastructure and workload scheduling
Strong expertise in distributed systems, scalability, reliability, and performance engineering
Experience with Kubernetes, container orchestration, or large-scale cluster management systems
Experience designing backend services or infrastructure platforms operating at production scale
Strong communication and collaboration skills across engineering and research teams
Bachelor’s degree in Computer Science, Engineering, or related field