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

Cuda Deep Learning Experiment Tracking Inference Model Training Signal Extraction

Candidate Availability

Hybrid · London · Required

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

About the Role

You will focus on developing alpha signal generation pipelines from data ingestion and feature engineering to model training and deployment. You will research and implement advanced deep learning architectures for short-horizon forecasting, optimise inference latency and robustness, integrate models into live trading systems, and continuously refine model quality through backtesting, live evaluation, and monitoring.

Requirements

Degree in Computer Science, Machine Learning, Applied Mathematics, or similar quantitative discipline
Strong programming skills in Python and familiarity with ML libraries
Proven track record applying ML or deep learning to real-world problems
Familiarity with time-series modeling, signal extraction, or high-frequency data
Experience developing ML infrastructure including data pipelines, experiment tracking, and versioning
Experience in finance, trading, or quantitative research (nice to have)
Publications, competition results (e.g., Kaggle), or open-source contributions (nice to have)
Familiarity with C++, CUDA, or low-latency systems (nice to have)

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 advanced 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 robustness and ensure models behave safely under live market conditions
Continuously refine model quality through systematic backtesting, live evaluation, and monitoring

Benefits

Performance-based compensation with significant earning potential
Pension
Private health insurance
Office in central London with amenities such as table tennis and foosball
Team meals and company-wide events
Flexible working from home and working hours
UK work permits and relocation assistance