Machine Learning Researcher
A research role applying deep learning to forecasting, feature engineering and optimisation problems at a quantitative market maker. You would own models end to end with direct impact on trading results.
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.
- Prototype deep learning models for market forecasting
- Engineer features and test architectures in PyTorch or JAX
- Carry techniques over from other ML fields to market problems
- Work with traders and developers to turn ideas into production features
What they ask for
Required
- 2+ years applying deep learning
- Python and modern DL frameworks
- Rapid prototyping
- Moving methods between problem domains
- Production mindset
- Collaboration with traders and engineers
Preferred
- Reinforcement learning
- C++ in production
- Core PyTorch or JAX development
- Education
- Not stated
- Experience
- 2+ years of applied deep learning
- Languages and tools
How to prepare
- Build deep models on noisy financial time series and show honest out-of-sample evaluation
- Study overfitting, leakage and cross-validation specific to finance
- Learn PyTorch internals and write custom training loops
- Read about reinforcement learning for execution and market making
What to study
- Financial Machine Learning
- Probability, Statistics and Financial Data
- Econometrics and Financial Time Series
- Python for Quantitative Finance
The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Virtu Financial’s site before applying: requirements, location and pay can change after we check them.
View the posting on Virtu Financial’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Virtu Financial and doesn’t handle applications.