Internship Duration
- Minimum of 8 Weeks
Responsibilities
- Model Design: Experiment with advanced ML and deep learning models to predict global equity trading signals.
- Data Engineering: Process high-frequency market data to engineer features for large-scale model training.
- Training & Deployment: Build and optimize training infrastructure to ensure rapid deployment.
- Prediction & Attribution: Integrate quantitative strategies and systematically attribute trading performance.
Requirements
- Academic Background: Ph.D. students graduating in 2026/2027/2028 in Computer Science, Artificial Intelligence, or related fields (exceptional candidates may be considered with relaxed criteria).
- Programming Skills: Strong proficiency in Python and mastery of mainstream deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and scientific computing libraries.
- Professional Qualities: A rigorous theoretical foundation in machine learning/deep learning, coupled with a deep understanding of model optimization, training pipelines, and system architecture.
- Personal Traits: A passion for technical research, strong self-drive, exceptional complex problem-solving skills, and a collaborative mindset.
- Language Skills: Excellent English reading and writing abilities, capable of seamlessly navigating technical documentation.
Bonus Points
- Significant research and practical experience with deep learning models, particularly applied to time-series or financial data.
- Top-tier rankings in Kaggle or other prestigious machine learning/data science competitions.
- Publications in premier conferences or journals covering machine learning, computer vision, or NLP.
- Familiarity with distributed computing (e.g., Ray, Dask, Spark) or high-performance computing (e.g., CUDA).
- Prior internship experience at leading quantitative trading or technology firms.