Training / AI Infrastructure

Genesis AI; Paris; On-site; EUR 90,000 - 150,000; Full time

Job summary

Genesis AI in Paris is seeking a senior ML infrastructure engineer to cut wall-clock time in foundation model training by profiling bottlenecks and optimizing the full stack from data pipelines to GPU kernels.

You will design distributed PyTorch training across multi-node GPU clusters, implement low-level CUDA/Triton code, and ensure scalable, robust, and highly utilized systems. Strong Python production skills and a system-minded approach are essential.

Qualifications

• 8+ years of experience in distributed systems, ML infra, or HPC.
• Proficient in Python, production-grade coding practices.
• Expertise in CUDA/cuDNN/Triton and kernel optimization.
• Experience with PyTorch distributed training on multi-node GPUs.
• Strong system-level approach to optimize CPU-GPU interactions.

Responsibilities

• Profile and eliminate bottlenecks to reduce wall-clock time in training stacks.
• Design, build, and optimize distributed PyTorch training across multi-node GPUs.
• Implement low-level code and integrate into high-level training frameworks.
• Optimize hardware utilization across CPU/GPU, memory, data throughput, and networking.
• Develop monitoring and debugging tools for large-scale runs.

Skills

Distributed systems

ML infrastructure

High-performance computing

Python

PyTorch

Kernel optimization

CPU–GPU optimization

Tools

CUDA

cuDNN

Triton

PyTorch

Job description

What You’ll Do

• 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

What You’ll Bring

• 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