Senior Distributed Training Engineer — Large-Scale GPU Systems

Speedrun Talent Network; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Luma is building distributed training systems for large-scale multimodal models across thousands of GPUs, enabling researchers to focus on innovation on top of reliable, scalable infrastructure.

This is hard PyTorch, CUDA, and distributed-systems work — advanced parallelism, training stability, and utilization across massive clusters. It fits an engineer who's solved real problems training foundation models at scale.

Qualifications

• Extensive distributed PyTorch training experience for foundation-model scale.
• 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

GPU clusters

NCCL/MPI

Containerization

Tools

Containerization

Job description

Luma is building distributed training systems for large-scale multimodal models across thousands of GPUs, enabling researchers to focus on innovation on top of reliable, scalable infrastructure.

This is hard PyTorch, CUDA, and distributed-systems work — advanced parallelism, training stability, and utilization across massive clusters. It fits an engineer who's solved real problems training foundation models at scale.