Machine Learning Researcher

Job description

## About Wintermute Wintermute is a technology unicorn and one of the largest algorithmic trading companies, specialising in digital assets. We provide liquidity across most cryptocurrency exchanges and trading platforms, a broad range of OTC trading solutions as well as supporting 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. At Wintermute, 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.](https://wintermute.com/about) ## 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.