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

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

• 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, * 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

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