Artificial Intelligence Engineer

About the Role

We’re looking for an AI-Engineer within Distributed LLM Training & Infrastructure to work on large-scale model training infrastructure across distributed GPU environments. This role focuses on improving how LLMs are trained at scale optimising performance, cost, and efficiency across multi-node systems.

Responsibilities

• Build and optimise distributed LLM training pipelines using PyTorch
• Work with frameworks such as Megatron-LM and DeepSpeed for large-scale training
• Improve multi-node GPU performance (throughput, memory usage, NCCL communication)
• Design and run benchmarking frameworks (tokens/sec, cost, MFU, latency)
• Develop standardised training recipes and playbooks for production-grade environments

What You’ll Work On

• Core LLM training systems (not application-layer AI)
• Distributed systems challenges across multi-GPU, multi-node setups
• Performance optimisation and scaling of large models in production environments

Ideal Background

• 5-7 Years Experience in distributed ML / ML systems
• Strong hands-on experience with PyTorch and multi-node, multi-GPU training
• Deep understanding of parallelism strategies (FSDP, tensor, pipeline)
• Exposure to Megatron-LM, DeepSpeed, or similar training frameworks
• Strong focus on benchmarking, optimisation, and improving training efficiency

Why This Role

• Work on high-impact problems in large-scale AI training and infrastructure
• High ownership within a lean, senior team
• Opportunity to define training standards and best practices for scalable AI systems