Machine Learning Engineer

We’re partnering with a high-performing, research-driven team in a systematic trading environment, tackling complex, real-world prediction and optimisation challenges.

This role sits at the intersection of machine learning research, mathematical modelling, and high-performance computing, with a focus on designing, testing, and deploying models in data-rich, noisy, and highly dynamic environments where precision, speed, and robustness are critical.

You’ll develop novel machine learning models for large-scale predictive systems, apply statistical and probabilistic techniques to real-world data, and design rigorous experiments to evaluate performance.

The work also includes simulation-driven approaches such as Monte Carlo and stochastic systems, alongside building high-performance implementations in Python and/or C++, collaborating closely with engineers and researchers to optimise models at scale.

We’re looking for candidates with a strong academic background in mathematics, physics, computer science, or a related field, with excellent programming skills in Python and/or C++ and a deep understanding of probability, statistics, optimisation, and numerical methods. You should have experience developing or researching machine learning models beyond standard libraries, with clear evidence of modelling under uncertainty and experience working with large-scale datasets and compute environments.

Strong additional signals include experience with stochastic processes, simulation-based modelling, or time-series data, as well as exposure to parallel computing, HPC, or low-latency systems, and any publications or research contributions in a relevant field.