As an engineer on the ML Compute team at Apple, you will focus on performance optimization for large-scale foundation model training on TPUs. Your work will involve profiling and optimizing JAX/XLA workloads, developing high-performance TPU kernels, and optimizing distributed training techniques. You will collaborate with cross-functional engineers to tackle complex ML training challenges while leading technical projects and mentoring others. This role emphasizes innovation, technical excellence, and teamwork.

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

• 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.
• 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.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.