Software Engineer, ML Performance

Responsibilities

• Analyze the performance of distributed training and inference workloads throughout the entire stack: model and framework, data loading and checkpointing, compiler and kernels, CUDA and NCCL, host and network.
• Identify the bottleneck, resolve it or suggest a solution, and provide the supporting evidence.
• Define the performance metrics tracked across a fleet of GPUs. Break them down into factors engineers need to debug. Decide which signals the platform collects first.
• Drive the analysis of regressions: segment workloads across machines, versions, and configurations to isolate the cause, and turn the method into analysis others can reuse.
• Evaluate automated diagnoses: judge whether a finding is accurate, identify misleading results and missed problems, and build the labeled cases that keep the analysis honest.
• Work closely with build partners on their real workloads, carry findings through to improvements, and feed what the product is missing back into the roadmap.
• What we need to see
• Bachelor’s degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.
• 12+ years of relevant software engineering experience, including hands-on optimization of GPU training or inference workloads in production.
• Demonstrated performance work across more than one layer of the stack, with a clear account of the baseline, evidence, alternative explanations and validated outcome.

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