Machine Learning Specialist

Machine Learning Engineer * We're partnering with a highly quantitative research organisation building some of the most advanced machine learning systems in industry. * Engineers in this team operate at the intersection of machine learning, distributed systems, and high-performance computing, helping scale modern AI workloads across a large GPU estate. The work spans distributed training, inference optimisation, compute infrastructure, systems design, and performance engineering. * You'll work directly with researchers to take cutting-edge ML ideas from prototype to production, solving problems that span software, hardware, networking, compilers, and large-scale distributed systems. * This is an opportunity to tackle technical challenges rarely seen outside leading AI labs and top-tier quantitative research firms. Responsibilities * Design and optimise large-scale training and inference systems for modern ML workloads. * Improve throughput, latency, GPU utilisation and training efficiency across distributed environments. * Build infrastructure and tooling that accelerates experimentation and model development. * Partner with researchers to productionise novel ML approaches. * Drive performance improvements across software, hardware and networking layers. * Influence the technical direction of critical ML infrastructure used across the organisation. What We're Looking For * Strong experience in Machine Learning Engineering, Research Engineering, ML Infrastructure, Distributed Systems or Performance Engineering. * Excellent software engineering skills in Python and/or C++. * Experience working with modern ML frameworks such as PyTorch, JAX or TensorFlow. * Experience training, deploying or optimising large-scale machine learning models. * Strong understanding of distributed systems, parallel computing and performance optimisation. * Degree in Computer Science, Mathematics, Physics, Engineering or a related quantitative discipline, or equivalent industry experience. Particularly Relevant Experience * Large-scale distributed training (DeepSpeed, FSDP, Megatron, Ray, DDP or similar). * GPU programming and optimisation (CUDA, Triton, NCCL, XLA, PTX). * Multi-GPU or multi-node training environments. * HPC, Kubernetes, Slurm or large-scale compute infrastructure. * Foundation models, LLMs, recommendation systems or large-scale deep learning. * Compiler technologies, kernel optimisation, inference optimisation or systems-level ML performance work. Why Join? * Work on some of the largest and most computationally intensive ML workloads in industry. * Solve challenging problems across distributed systems, GPU computing, machine learning infrastructure and performance optimisation. * Collaborate closely with exceptional researchers, engineers and quantitative scientists. * Significant autonomy and ownership from day one. * Deep investment in compute infrastructure and engineering excellence. * Competitive compensation and bonus structure.