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
At Technip Energies, we pride ourselves on being an exceptional employer, offering a dynamic and inclusive work culture that fosters collaboration and innovation. Our London office is at the forefront of the energy transition, providing employees with unique opportunities to contribute to high-impact projects in the Power Generation sector while enjoying a supportive environment that prioritises professional growth and environmental responsibility.
