Develop ML-based models for alpha generation using high-frequency data, maintain data pipelines, optimize model performance, and collaborate with teams.
About Wintermute
Wintermute is one of the largest crypto native algorithmic trading companies in digital assets. We provide liquidity across most cryptocurrency exchanges and trading platforms, a broad range of OTC trading solutions as well as support high profile blockchain projects and traditional financial institutions moving into crypto. Wintermute also has a Wintermute Ventures arm that invests in early stage DeFi projects.
Wintermute was founded in 2017 and has successfully navigated industry cycles. Culturally, we combine the best of the two worlds: the technology standards of high-frequency trading firms in traditional markets and the innovative and entrepreneurial culture of technology startups. To Wintermute digital assets is not just another asset class, we believe in the innovative potential of blockchain, the fundamental innovations, we have a long-term view on the digital asset market and are taking a leadership position in building an innovative and compliant market. You can read more here.
Working at Wintermute
You are an experienced machine learning engineer or researcher with a strong track record in applied deep learning, ideally in domains involving high-frequency or large-scale time-series data.
You will focus on developing alpha signal generation pipelines from data ingestion and feature engineering to model training and deployment - in collaboration with our trading and infrastructure teams.
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 tailored to 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; ensure models behave safely under live market conditions.
- Continuously refine model quality through systematic backtesting, live evaluation, and monitoring.
Hard Skills 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/DL to real-world problems.
- Familiarity with time-series modeling, signal extraction, or high-frequency data.
- Experience in developing ML infrastructure (data pipelines, experiment tracking, versioning).
Nice to have requirements:
- Experience in finance, trading, or quantitative research (not required).
- Publications, competition results (e.g., Kaggle, academic ML contests), or open-source contributions.
- Familiarity with C++, CUDA, or low-latency systems.
Here is why you should join our dynamic team:
- Opportunity to work at one of the world's leading algorithmic trading firms
- Engaging projects offering accelerated responsibilities and ownership compared to traditional finance environments.
- A vibrant working culture with team meals, festive celebrations, gaming events and company wide team building events.
- A Wintermute-inspired office in central London, featuring an array of amenities such as table tennis and foosball, personalized desk configurations, a cozy team breakout area with games.
- Great company culture: informal, non-hierarchical, ambitious, highly professional with a startup vibe, collaborative and entrepreneurial.
- A performance-based compensation with a significant earning potential alongside standard perks like pension and private health insurance.
Note:
- Although we are unable to accept fully remote candidates, we support significant flexibility about working from home and working hours.
- We offer UK work permits and help with relocation
Top Skills
C++
Cuda
Ml Libraries
Python
Wintermute London, England Office
London, United Kingdom
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