Machine Learning Engineer

ML Research Engineering:

• 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.

ML Performance Engineering:

• 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.

Across both areas:

• 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.

Core, regardless of focus area:

• 3+ years' 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.

Depth in one or more of the following:

• 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.

Also a plus:

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

About Us

IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.