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About the Role
This internship offers a unique educational experience, allowing participants to work alongside experienced Machine Learning Researchers on projects relevant to systematic trading strategies. Interns will gain insights into market dynamics through challenging classes and practical model training, utilizing vast datasets and computing resources.
Responsibilities
- Work closely with full-time machine learning researchers on projects.
- Conduct end-to-end studies of unexplored datasets.
- Explore new modeling paradigms for complex problems.
- Consider novel approaches to current challenges.
- Dive deep into market data.
- Tune hyperparameters.
- Debug training issues.
- Analyze model predictions.
Requirements
- Undergraduate, PhD student, or postdoc with practical experience in ML problems.
- Interest in applying logical and mathematical thinking to problems.
- Curiosity about the machine learning landscape.
- Excitement to apply state-of-the-art techniques.
- Ability to rapidly implement and iterate on ideas in Python and preferred ML framework.
- Eagerness to ask questions, admit mistakes, and learn new things.
Skills
- Machine Learning
- Python
- ML framework implementation
- Data analysis
- Model training
- Hyperparameter tuning
- Debugging
Experience Level
- Internship
Education Level
- Undergraduate
- PhD student
- Postdoc
About the Company
- Jane Street blurs the lines between research, technology, and trading.
- Access to petabytes of data, a large computing cluster, and a GPU cluster.
- Trading environment presents challenges such as large models, nonstationary datasets, and competitive multi-agent dynamics.
- Focus on novel techniques driven by trading challenges.