Member of Technical Staff, Capacity & Efficiency Infrastructure - MAI Superintelligence Team

• Design, implement, test, and optimize distributed training infrastructure in Python and C++ for large-scale GPU clusters.

• Build and evolve telemetry systems to provide visibility into infrastructure & ML model performance, utilization, and cost related metrics

• Profile, benchmark, and debug performance bottlenecks across compute, memory, networking, and storage subsystems

• Drive architectural improvements across various ML services which deliver measurable efficiency improvements

• Build and evolve tools to automatically provide insights and recommendations to improve fleet-wide efficiency

• Optimize collective communication libraries (e.g., NCCL) for emerging NVLink and InfiniBand topologies

• Partner with ML researchers and infrastructure engineers to understand their plans and future needs and develop plans to balance growth with efficiency

• Collaborate with hardware teams to optimize for next-generation accelerators (NVIDIA, MAIA, and beyond)

• Embody our Culture and Values.

• Bachelor's Degree in Computer Science, or related technical discipline AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python

• OR equivalent experience, * Bachelor's Degree in Computer Science or related technical field AND 10+ years technical engineering experience with coding in languages including, but not limited to, C++ or Python OR Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C++ or Python
• OR equivalent experience

• Deep understanding of the fundamentals of GPU architectures and DL/LLM architectures
• Deep experience in profiling and analyzing performance in large-scale distributed computing systems.
• Deep experience in profiling and analyzing performance in ML models especially GenAI models
• Experience with low-level GPU programming (CUDA, Triton, NCCL) and frameworks such as PyTorch or JAX.
• Experience in leading technical projects and supporting architectural decisions with data.
• Experience building infrastructure for large-scale machine learning or generative AI workloads.
• Experience in networking (InfiniBand, NVLink), storage systems, or distributed training parallelisms.
• Track record of contributing to high-performance computing or large-scale AI infrastructure projects.

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