Machine Learning Engineer

About the role

Machine Learning Engineers at IMC play a pivotal role in building the systems that drive training and deployment of large-scale ML models across IMC's global operations. They collaborate with quantitative researchers, hardware specialists and software engineers to build robust systems that get the most out of GPU acceleration, distributed computing and the broader open-source ML ecosystem. ML Engineers' work directly shapes how fast we can experiment, iterate and deploy, accelerating the cycle that turns a research idea into a live trading strategy.

This is a unique opportunity to solve problems at the intersection of advanced machine learning and trading, where your contributions will shape the future of IMC's technology and trading capabilities.

As a team, our Machine Learning Engineers broadly work across two areas: ML Research Engineering, focused on the systems and frameworks researchers use to train, evaluate and iterate on models at scale; and ML Performance Engineering, focused on making ML workloads execute as efficiently as possible, typically through GPU and low-level systems optimisation. These aren't rigid tracks, and there is overlap between them. You will bring depth of expertise in at least one of these focus areas and collaborate closely with global team members.

Your Core Responsibilities:

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.

Your Skills and Experience:

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.