Training / AI Infrastructure

Genesis; Paris; Presencial; EUR 120.000 - 180.000; Jornada completa

Descripción de la vacante

Genesis is seeking a senior ML infrastructure engineer to optimize large-scale PyTorch training systems on multi-node GPU clusters. You will profile bottlenecks, design distributed training stacks, and implement high-performance kernels.

You will work with Python daily, CUDA and Triton, and collaborate with teams to maximize hardware utilization across data pipelines, kernels, memory and networking for scalable AI workloads.

Formación

• 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.

Responsabilidades

• Drive down wall-clock time to convergence by profiling and eliminating bottlenecks across the foundation model training 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 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.

Conocimientos

Distributed systems

Python

CUDA

PyTorch

Performance profiling

Kernel optimization

Herramientas

CUDA

cuDNN

Triton

GPU kernels

Descripción del empleo

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