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.

• 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

• 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 ...