Machine Learning Engineer

A leading hedge fund is hiring a Machine Learning Engineer to work across model development, inference, and ML infrastructure for systematic trading.

This role sits close to both machine learning research and production engineering. You would help build models, improve how they are trained and evaluated, and make sure they can be used reliably in a live trading or investment environment.

The work could include deep learning models, time series forecasting, signal generation, feature engineering, model evaluation, inference optimisation, training pipelines, data workflows, and production ML systems. You may also work on model serving, latency improvements, distributed training, experiment tracking, and tools that help researchers move from idea to deployment faster.

This is not just an infrastructure role. It is also not a pure research role. It is best suited to someone who enjoys working across the full ML lifecycle: building models, improving performance, scaling systems, and making research usable in production.

You might be a strong fit if you have worked as a Machine Learning Engineer, Research Engineer, Applied Scientist, ML Software Engineer, or Software Engineer on ML-heavy systems.

Good backgrounds include:

Strong Python and/or C++

Experience building, training, evaluating, or deploying machine learning models

Exposure to deep learning, time series, NLP, reinforcement learning, forecasting, or large-scale modelling

Experience with inference, model serving, latency optimisation, or production ML systems