AI Training Systems Engineer (Distributed HPC)

Genesis AI; Paris; On-site; EUR 90,000 - 140,000; Full time

Job summary

Genesis AI in Paris seeks a senior ML infrastructure engineer to optimize distributed training stacks across data pipelines and GPU kernels. You will design, build, and optimize multi-node PyTorch systems for scalable, robust performance.

Responsibilities include implementing low-level CUDA/cuDNN/Triton code, balancing CPU-GPU workloads, and developing monitoring tools to diagnose performance regressions, ensuring high hardware utilization in large-scale runs.

Qualifications

• 8+ years in distributed systems, ML infrastructure, or HPC.
• Production-grade Python expertise.
• Deep CUDA/cuDNN/Triton knowledge and kernel optimization.
• Experience with PyTorch multi-node training and data, context, pipeline, and model parallelism.

Responsibilities

• Profile bottlenecks across data pipelines, GPU kernels, and training stacks.
• Design and optimize distributed training systems for multi-node GPUs.
• Implement low-level CUDA/CuDNN/Triton code and integrate with PyTorch.
• Tune CPU-GPU balance, memory management, data throughput, and networking.
• Develop monitoring and debugging tools for large-scale runs.

Skills

Distributed systems

ML infrastructure

High-performance computing

Python production-grade

PyTorch training

System-level tuning

Tools

CUDA/cuDNN/Triton

Job description

Genesis AI in Paris seeks a senior ML infrastructure engineer to optimize distributed training stacks across data pipelines and GPU kernels. You will design, build, and optimize multi-node PyTorch systems for scalable, robust performance.

Responsibilities include implementing low-level CUDA/cuDNN/Triton code, balancing CPU-GPU workloads, and developing monitoring tools to diagnose performance regressions, ensuring high hardware utilization in large-scale runs.