Wintermute helps traders and institutions access digital asset liquidity through algorithmic market making and OTC services. Users can trade various tokens, access derivatives markets, and execute large orders without market impact. Wintermute provides deep liquidity across centralized and decentralized platforms.

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Skills

Backtesting C Cuda Data-Pipeline Deep Learning Experiment Tracking Feature Engineering Forecasting High-Frequency Data Low-Latency Systems Machine-Learning Model Deployment Python Signal Extraction Time Series Versioning

Candidate Availability

Onsite · London · Required

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

Develop machine-learning alpha-generation models using high-frequency order-book and market-microstructure data, build data pipelines and feature-extraction workflows, research deep-learning architectures for short-horizon forecasting, deploy models into live trading environments, optimise inference latency, and improve model quality through backtesting and monitoring.

Requirements

Degree in Computer Science, Machine Learning, Applied Mathematics, or a similar quantitative discipline
Strong programming skills in Python and familiarity with machine-learning libraries
Proven track record applying machine learning and deep learning to real-world problems
Familiarity with time-series modelling, signal extraction, or high-frequency data
Experience developing machine-learning infrastructure, including data pipelines, experiment tracking, and versioning
Experience in finance, trading, or quantitative research is not required
Publications, competition results, or open-source contributions are beneficial
Familiarity with C++, CUDA, or low-latency systems is beneficial

Responsibilities

Develop ML-based alpha-generation models using high-frequency order-book and market-microstructure data
Design and maintain data pipelines, preprocessing, and feature-extraction workflows for streaming tick data
Research and implement deep-learning architectures for short-horizon forecasting and signal extraction
Collaborate with quant researchers and developers to integrate models into live trading environments
Optimise inference latency and model robustness
Ensure models behave safely under live market conditions
Refine model quality through backtesting, live evaluation, and monitoring

Benefits

Team meals
Festive celebrations
Gaming events
Company-wide team-building events
Office amenities including table tennis and foosball
Flexible working from home
Flexible working hours
Pension
Private health insurance
UK work permits
Relocation assistance
Performance-based compensation