The Role
As a Quantitative Research Intern, you will assist in developing machine learning-driven trading strategies, analyzing data, and collaborating with senior researchers.
Summary Generated by Built In
Human Intuition. Machine Intelligence. Relentless Exploration.
The Role:
Responsibilities:
You Bring:
We Offer:
Exceptional trading emerges where human intuition meets frictionless experimentation. Our platform and processes enable traders to rapidly investigate ideas, identify emergent patterns, and convert insights into live strategies. This synthesis creates a flywheel of discovery — the key to our pursuit of excellence.
We are looking for a Quantitative Research Intern to help build machine learning-driven statistical arbitrage and high-frequency trading strategies in U.S. equities. You will work closely with our senior researchers, using large-scale data and quantitative models to predict short-term price movements, design models, and capture market inefficiencies.
- Alpha & Strategy Research: Explore, model, and validate new predictive signals using large, complex datasets.
- Machine Learning Application: Apply advanced machine learning and deep learning architectures to uncover hidden patterns and inefficiencies in financial markets.
- Rigorous Validation: Stress-test your hypotheses, identify hidden biases, and analyze model performance using the team’s backtesting frameworks.
- Collaborative Innovation: Participate in technical discussions and present your research findings to the broader team.
- Education: Currently in penultimate or final year of a PhD program in a highly quantitative field such as Computer Science, Statistics, Mathematics, Physics, or Engineering (minimum 3.5 GPA required).
- Required Skills:
- Programming: Strong proficiency in Python and its scientific computing stack (Pandas, NumPy, SciPy, scikit-learn).
- Deep Learning: Practical experience building and training RNN (Recurrent Neural Network) and Transformer architectures.
- Machine Learning: Hands-on experience with predictive modeling, including regression, classification, clustering, as well as bagging and boosting algorithms (e.g., XGBoost, LightGBM).
- Behavioral: Strong intellectual curiosity paired with low ego. A relentless drive to find the truth in data, the ability to clearly defend your research methodology, and the eagerness to learn how theoretical models translate to financial markets.
- Competitive Pedigree: Demonstrated success in competitive environments, such as AI/machine learning competitions (e.g., Kaggle) or programming competitions (e.g., ICPC, Putnam).
The estimated weekly base salary for this role is $3,350. The final offer will be based on job-related factors such as relevant experience, skills, and location.
We believe that working together in person is key to tackling the complex challenges we face, which is why this role will require being in the office 4 days per week. In-office perks include lunch and a great location with amazing colleagues.
Our top priority is our people. We invest in a culture that promotes togetherness—supporting one another through challenges and celebrating our collective successes. We believe that modern workplaces thrive on diverse, high-performance workforces across ideas, cultures, and experiences. We are committed to making this environment a daily reality and are proud to be an equal opportunity employer.
Top Skills
Lightgbm
Numpy
Pandas
Python
Rnn
Scikit-Learn
Scipy
Transformer
Xgboost
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The Company
What We Do
Exceptional trading emerges where human intuition meets frictionless experimentation. Our platform and processes enable traders to rapidly investigate ideas, identify emergent patterns, and convert insights into live strategies. This synthesis creates a flywheel of discovery — the key to our pursuit of excellence.
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