Systematic Fast Trading Intern (MLE / QR)
A summer internship in Man Systematic's Fast Trading Strategies team, which builds high-frequency strategies across futures and cash markets. Interns join either a machine-learning engineering or a quantitative research track.
What you’d work on
Our reading of the posting: what a new hire in this role would most likely spend their first months on.
- Research predictive signals from high-frequency tick data using ML
- Turn promising models into candidate trading strategies
- On the engineering track, build MLOps and research tooling that others use
- Test models and prepare them for deployment with the MLOps group
What they ask for
Required
- PhD student in a highly technical field, in the penultimate year
- Strong academic record
- Strong programming, especially Python
- Knowledge of ML algorithms and research workflow
- Comfort with statistics
Preferred
- Previous ML or statistical research internship in finance, tech or academia
- Education
- PhD students in highly technical programmes; penultimate-year only
- Experience
- Prior research internship preferred; students only
- Languages and tools
- Markets
- multi-asset, futures
How to prepare
- Fit tree and neural models to noisy time-series and test them out of sample
- Learn how limit order books and tick data behave
- Build a reproducible ML pipeline end to end
- Read introductory market microstructure material
What to study
- Financial Machine Learning
- Market Microstructure and Execution
- Statistical Arbitrage
- Python for Quantitative Finance
The HFT Quantitative Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Man Group’s site before applying: requirements, location and pay can change after we check them.
View the posting on Man Group’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Man Group and doesn’t handle applications.