Training / AI Infrastructure

Genesis AI; Greater London; Hybrid; GBP 120,000 - 170,000; Full time

Job summary

Genesis AI seeks a senior ML infrastructure engineer to optimize distributed training pipelines and systems on multi-node GPU clusters in London. You will profile bottlenecks, implement low-level kernels, and integrate CUDA/cuDNN/Triton into high-level frameworks to maximize throughput and utilization.

The role requires deep expertise in distributed systems, Python, and PyTorch with a track record of hardware-aware optimizations across CPU/GPU boundaries and data movement.

Qualifications

• 8+ years of experience in distributed systems, ML infrastructure, or HPC.
• Production-grade Python experience.
• Low-level performance mastery with CUDA/cuDNN/Triton and kernel optimization.
• Experience scaling PyTorch training jobs with data, context, pipeline, and model parallelism.
• System-level mindset tuning hardware–software interactions for max utilization.

Responsibilities

• Profile and remove bottlenecks to reduce wall-clock convergence time.
• Design and optimize distributed training systems for multi-node GPU clusters.
• Implement low-level code (CUDA/cuDNN/Triton) and integrate into training frameworks.
• Optimize workloads for CPU/GPU balance, memory, data throughput, and networking.
• Develop monitoring and debugging tools for large-scale runs.

Skills

Distributed systems

Python

Performance optimization

PyTorch

Hardware-tuning

Tools

CUDA

cuDNN

Triton

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