Machine Learning Specialist

• 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.

• 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.