Distributed ML Engineer - Large-Scale GPU Training

Luma; United Kingdom; On-site; GBP 120,000 - 180,000; Full time

Job summary

Luma is seeking an engineer to build distributed training systems for its large-scale multimodal models across thousands of GPUs. You’ll work on difficult PyTorch, CUDA, and distributed-systems challenges to maximize stability and utilization in massive clusters.

Ideal candidates will have extensive experience with foundation-model training, GFP clusters, and the ability to implement advanced parallelization strategies while building robust monitoring and tooling.

Qualifications

• Extensive distributed PyTorch training and parallelisms in foundation-model training.
• Deep understanding of GPU clusters, networking, and storage systems.
• Familiarity with communication libraries (NCCL, MPI) and distributed-system optimization.

Responsibilities

• Design, implement, and optimize efficient distributed training systems for models across thousands of GPUs.
• Research and implement advanced parallelization (FSDP, Tensor Parallel, Pipeline Parallel, Expert Parallel).
• Build monitoring, visualization, and debugging tools for large-scale training runs.
• Optimize training stability, convergence, and resource utilization across massive clusters.

Skills

Distributed PyTorch training

GPU clusters & networking

Distributed-system optimization

Tools

NCCL

MPI

Containerization

Job description

Luma is seeking an engineer to build distributed training systems for its large-scale multimodal models across thousands of GPUs. You’ll work on difficult PyTorch, CUDA, and distributed-systems challenges to maximize stability and utilization in massive clusters.

Ideal candidates will have extensive experience with foundation-model training, GFP clusters, and the ability to implement advanced parallelization strategies while building robust monitoring and tooling.