Machine Learning Engineer

Machine Learning EngineerWe'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.ResponsibilitiesDesign 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.Requirements6+ 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 ExperienceDistributed 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 PreferredExperience 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.