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

Cupertino, California, United States -

Full Time

Start Date

Immediate

Expiry Date

19 Oct, 26

Salary

0.0

Posted On

21 Jul, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

TPU Scheduling, Distributed Systems, Kubernetes, Python, Go, C++, ML Infrastructure, Resource Management, Orchestration Systems, Performance Optimization, Fault Tolerance, JAX, PyTorch, TensorFlow, Ray, Pathways

Industry

Computers and Electronics Manufacturing

Description

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! 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 Internal Details Key Matching Terms #ASEENG #ASKENG 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. 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 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

Responsibilities

Lead the design and development of scheduling and orchestration systems for large-scale TPU workloads across multi-region clusters. Manage thousands of accelerators to ensure reliable and efficient execution of large-scale training and inference jobs.