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

Apple • Santa Clara, CA

13h

About The Position

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.

Requirements

• 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

Nice To Haves

• 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

Responsibilities

• Lead the design and development of scheduling and orchestration systems for large-scale TPU workloads across multi-region clusters.
• Work on distributed systems that manage thousands of accelerators and enable reliable, efficient execution of large-scale training and inference jobs.
• Focus on scheduling algorithms, cluster lifecycle management, workload orchestration, reliability engineering, and performance optimization.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior