Machine Learning Engineer

The responsibilities below are representative of the role — the exact mix depends on your team, area of expertise and location.

• Develop large-scale distributed training pipelines that manage complex datasets and models.
• Partner with quantitative researchers to automate ML experiments, hyperparameter tuning and model retraining.
• Work with HPC specialists to optimise workflows, improve training speed and reduce compute cost.

• Build and optimise low-latency inference pipelines, ensuring models deliver real-time predictions in production systems.
• Maximise performance in training and inference using GPU hardware and acceleration libraries.

• Extend and improve the internals of machine learning frameworks and open-source tools to raise their performance for our workloads.
• Design scalable model infrastructure capable of handling high-volume trading data and delivering real-time, high-accuracy predictions.
• Evaluate and roll out third-party tools that enhance model development, training and inference capabilities.

• 3+ years of experience in machine learning, with a focus on training or inference systems.
• Production software engineering — Python, testing discipline and genuine ownership of systems once they ship.
• Machine learning fundamentals — you understand modern ML workloads beyond high-level APIs and can reason about model behaviour, correctness and performance at the level relevant to your area of expertise.
• A systems mindset — you reach for measurement before opinion on performance, scale or reliability issues, and can make sound build-versus-buy decisions.
• Strong collaboration — comfortable explaining the reasoning behind your technical decisions, working through differing perspectives with researchers and adapting your approach when the evidence supports it.

• Distributed training and large-scale systems — parallelism strategy, data pipelines and checkpointing; frameworks such as PyTorch, TensorFlow or JAX.
• GPU performance and low-level optimisation — reasoning about memory, compute and latency at the device level, and optimising performance across kernels, compilers and hardware (e.g. CUDA, Triton or equivalent).
• Production inference systems — hands-on experience building and hardening real-time, low-latency prediction pipelines in high-performance environments.
• Numerics and correctness at scale — low-precision effects, KV caching, batching, and knowing which optimisations stay bit-exact.

Technologies listed above are examples rather than requirements; equivalent depth developed using other stacks or hardware platforms is equally relevant.

• Exposure to cloud platforms and orchestration tools.
• A track record of contributing to open-source projects in machine learning, data science or distributed systems.