What You'll Do
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Drive down wall-clock time to convergence by profiling and eliminating bottlenecks across the foundation model training stack stack, from data pipelines to GPU kernels
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Design, build, and optimize distributed training systems (PyTorch) for multi-node GPU clusters, ensuring scalability, robustness, and high utilization
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Implement efficient low-level code (CUDA, cuDNN, Triton, custom kernels) and integrate it seamlessly into high-level training frameworks
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Optimize workloads for hardware efficiency: CPU/GPU compute balance, memory management, data throughput, and networking
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Develop monitoring and debugging tools for large-scale runs, enabling rapid diagnosis of performance regressions and failures
What You'll Bring
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Deep experience in distributed systems, ML infrastructure, or high-performance computing (8+ years)
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Production-grade expertise in Python
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Low-level performance mastery: CUDA/cuDNN/Triton, CPU-GPU interactions, data movement, and kernel optimization
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Scaling at the frontier: experience with PyTorch and training jobs using data, context, pipeline, and model parallelism
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System-level mindset with a track record of tuning hardware-software interactions for maximum utilization
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Training / AI Infrastructure employer: Genesis AI
Genesis is an exceptional employer for those looking to make a significant impact in the AI and robotics field. With a competitive salary, meaningful equity, and a hybrid work model, employees enjoy a dynamic work culture that fosters innovation and collaboration. The opportunity to shape the recruiting function from the ground up, alongside industry leaders, ensures that team members can grow professionally while tackling some of the most challenging problems in technology today.
