AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - Pre-training Infrastructure

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 an engineer on the ML Compute team, your work will include:

Drive performance optimization for large-scale foundation model training on TPUs, focusing on efficiency, throughput, and scalability

Profile and optimize JAX/XLA workloads across compute, memory, communication, and compilation.

Develop and optimize high-performance TPU kernels for critical ML operations such as attention and Mixture-of-Experts (MoE)

Optimize distributed training techniques, sharding strategies, and collective communication over TPU interconnects (ICI/Fabric)

Research and implement new techniques across the JAX, XLA, and TPU stack to improve end-to-end training performance

Develop performance profiling, benchmarking, and automated tuning capabilities for large-scale training workloads.

Collaborate with cross-functional engineers to solve large-scale ML training challenges

Lead complex technical projects and mentor engineers in areas of your expertise

Cultivate a team centered on collaboration, technical excellence, and innovation

Preferred Qualifications

Advanced degree in Computer Science, Engineering, or a related field

Experience with accelerators such as TPU or GPU and understanding of accelerator architecture and performance characteristics

Experience with JAX, XLA, PyTorch or other ML compiler/runtime stacks

Experience developing or optimizing accelerator kernels using Pallas, Triton, CUDA, or similar technologies

Experience optimizing large-scale foundation model training and distributed communication

Minimum Qualifications

6+ years of experience building or optimizing high-performance ML or distributed systems

Proficient in Python or other relevant programming languages

Strong understanding of distributed systems, parallel computing, and performance optimization

Experience profiling and optimizing compute-, memory-, or communication-intensive workloads

Ability to clearly communicate complex technical problems and collaborate with partners to develop solutions

Bachelor's degree in Computer Science, Engineering, or a related field

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.