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.