Machine Learning Research Internship – PhD: 2027
A 10-week internship for PhD students in the machine learning group, which models noisy market data. It is run in the US with full-time placement in Hong Kong afterwards.
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 ML models that find patterns in noisy, non-stationary financial data
- Run experiments with PyTorch or TensorFlow and report results
- Work with researchers and traders to improve existing models
- Take part in the firm's education sessions on ML, quant and trading
What they ask for
Required
- Current PhD in CS, ML, statistics, physics or applied maths
- Applied machine learning experience
- Publications at top ML conferences
- PyTorch and TensorFlow
- Education
- Currently pursuing a PhD in computer science, machine learning, statistics, physics, applied mathematics or similar
- Experience
- PhD students with research or professional ML experience
- Languages and tools
How to prepare
- Train a sequence model on noisy financial time series and test it out of sample
- Study non-stationarity and regime shifts in return data
- Reproduce a recent ML paper in PyTorch
- Practise explaining model choices and failure modes
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 Susquehanna International Group (SIG)’s site before applying: requirements, location and pay can change after we check them.
View the posting on Susquehanna International Group (SIG)’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Susquehanna International Group (SIG) and doesn’t handle applications.