AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - Pre-training Infrastructure
AI & ML Engineering
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!
As an engineer on the ML Compute team, your work will include:\\n\\n• Drive performance optimization for large-scale foundation model training on TPUs, focusing on efficiency, throughput, and scalability \\n• Profile and optimize JAX/XLA workloads across compute, memory, communication, and compilation. \\n• Develop and optimize high-performance TPU kernels for critical ML operations such as attention and Mixture-of-Experts (MoE) \\n• Optimize distributed training techniques, sharding strategies, and collective communication over TPU interconnects (ICI/Fabric) \\n• Research and implement new techniques across the JAX, XLA, and TPU stack to improve end-to-end training performance \\n• Develop performance profiling, benchmarking, and automated tuning capabilities for large-scale training workloads. \\n• Collaborate with cross-functional engineers to solve large-scale ML training challenges \\n• Lead complex technical projects and mentor engineers in areas of your expertise \\n• Cultivate a team centered on collaboration, technical excellence, and innovation
6+ years of experience building or optimizing high-performance ML or distributed systems\\nProficient in Python or other relevant programming languages\\nStrong understanding of distributed systems, parallel computing, and performance optimization\\nExperience profiling and optimizing compute-, memory-, or communication-intensive workloads\\nAbility to clearly communicate complex technical problems and collaborate with partners to develop solutions\\nBachelor"s degree in Computer Science, Engineering, or a related field
Advanced degree in Computer Science, Engineering, or a related field\\nExperience with accelerators such as TPU or GPU and understanding of accelerator architecture and performance characteristics\\nExperience with JAX, XLA, PyTorch or other ML compiler/runtime stacks\\nExperience developing or optimizing accelerator kernels using Pallas, Triton, CUDA, or similar technologies\\nExperience optimizing large-scale foundation model training and distributed communication
As an engineer on the ML Compute team, your work will include:\\n\\n• Drive performance optimization for large-scale foundation model training on TPUs, focusing on efficiency, throughput, and scalability \\n• Profile and optimize JAX/XLA workloads across compute, memory, communication, and compilation. \\n• Develop and optimize high-performance TPU kernels for critical ML operations such as attention and Mixture-of-Experts (MoE) \\n• Optimize distributed training techniques, sharding strategies, and collective communication over TPU interconnects (ICI/Fabric) \\n• Research and implement new techniques across the JAX, XLA, and TPU stack to improve end-to-end training performance \\n• Develop performance profiling, benchmarking, and automated tuning capabilities for large-scale training workloads. \\n• Collaborate with cross-functional engineers to solve large-scale ML training challenges \\n• Lead complex technical projects and mentor engineers in areas of your expertise \\n• Cultivate a team centered on collaboration, technical excellence, and innovation
6+ years of experience building or optimizing high-performance ML or distributed systems\\nProficient in Python or other relevant programming languages\\nStrong understanding of distributed systems, parallel computing, and performance optimization\\nExperience profiling and optimizing compute-, memory-, or communication-intensive workloads\\nAbility to clearly communicate complex technical problems and collaborate with partners to develop solutions\\nBachelor"s degree in Computer Science, Engineering, or a related field
Advanced degree in Computer Science, Engineering, or a related field\\nExperience with accelerators such as TPU or GPU and understanding of accelerator architecture and performance characteristics\\nExperience with JAX, XLA, PyTorch or other ML compiler/runtime stacks\\nExperience developing or optimizing accelerator kernels using Pallas, Triton, CUDA, or similar technologies\\nExperience optimizing large-scale foundation model training and distributed communication