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Training / AI Infrastructure

Genesis· Engineering & Research
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Hybrid (London)Full-timePosted 1mo ago

About the role

Role focused on optimizing and scaling training infrastructure for foundation models, with emphasis on distributed systems, low-level optimization, and monitoring across large-scale GPU clusters.

Responsibilities

  • 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
  • Design, build, and optimize distributed training systems (PyTorch) for multi-node GPU clusters, ensuring scalability, robustness, and high utilization
  • Implement efficient low-level code (CUDA, cuDNN, Triton, custom kernels) and integrate it seamlessly into high-level training frameworks
  • Optimize workloads for hardware efficiency: CPU/GPU compute balance, memory management, data throughput, and networking
  • Develop monitoring and debugging tools for large-scale runs, enabling rapid diagnosis of performance regressions and failures

Qualifications

  • Deep experience in distributed systems, ML infrastructure, or high-performance computing (8+ years)
  • Production-grade expertise in Python
  • Low-level performance mastery: CUDA/cuDNN/Triton, CPU–GPU interactions, data movement, and kernel optimization
  • Scaling at the frontier: experience with PyTorch and training jobs using data, context, pipeline, and model parallelism
  • System-level mindset with a track record of tuning hardware–software interactions for maximum utilization

Skills

Required:distributed systemsPythonCUDA/cuDNN/TritonPyTorchhardware–software tuning
Last updated Sep 10, 2026. Interested? Apply on the company site.

About Genesis

A new future is at hand.

We are a new generation of robotics builders, united by a shared mission to push the boundaries of physical AI.

AIGENEGenesisfrontier-labsheadline-ai-europe-100-2026intelligencerobotics
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