Machine Learning Engineer

Machine Learning Engineer We're partnering with a highly quantitative research organisation building large-scale machine learning systems in a performance-critical environment. This role sits at the intersection of machine learning, distributed systems, and high-performance computing, with a focus on scaling modern ML workloads and improving the efficiency of training and inference for large models. Responsibilities * Design and optimise large-scale training and inference systems. * Improve throughput, latency, memory efficiency, and GPU utilisation across distributed workloads. * Partner with researchers to translate new ML ideas into scalable production systems. * Build infrastructure and tooling that accelerates experimentation, model development, and deployment. * Drive technical direction across performance-critical ML systems and compute infrastructure. * Solve challenging problems spanning software, hardware, compilers, and distributed computing. Requirements * 6+ years’ experience in Machine Learning Engineering, Research Engineering, ML Infrastructure, Distributed Systems, or Performance Engineering. * Strong Python and/or C++ development experience. * Deep understanding of modern ML frameworks including PyTorch, JAX, or TensorFlow. * Experience training, deploying, or optimising large-scale machine learning models. * Strong understanding of parallel computing, distributed systems, and performance optimisation. * Degree (or equivalent experience) in Computer Science, Mathematics, Physics, Engineering, or a related quantitative discipline. Highly Relevant Experience * Distributed training technologies such as DeepSpeed, FSDP, Megatron, Ray, DDP or similar. * GPU programming and optimisation (CUDA, Triton, NCCL, XLA, PTX). * Multi-GPU or multi-node training environments. * HPC, Slurm, Kubernetes, large-scale compute platforms, or cloud-based training infrastructure. * Foundation models, LLMs, recommendation systems, ranking systems, or large-scale deep learning. * Training efficiency, inference optimisation, compiler technologies, kernel optimisation, or systems-level ML performance work. Strongly Preferred * Experience working with billion-parameter models or large-scale distributed training workloads. * Contributions to ML infrastructure, training frameworks, open-source projects, or large-scale AI systems. * Experience owning performance-critical systems in production environments. * Publications or demonstrated technical expertise in machine learning systems, distributed computing, or optimisation.