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Machine Learning Researcher

Wintermute

Job Location:

London - UK

Monthly Salary: Not provided by the employer

Posted: 20 November 2025 (30+ days ago)

Application Deadline: 15 September 2026

Vacancies: 1 Vacancy

The job posting is outdated and position may be filled

Job Summary

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 morehere.

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 worlds 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

We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.

Required Experience: