Campus AI Research Engineer – Deep Learning Intern
Summary
Research and build high-performance ML systems for quantitative finance, integrating deep-learning models into low-latency trading pipelines using C++, Python, CUDA, and PyTorch/JAX/TensorFlow.
You will apply advanced machine-learning techniques to challenging quantitative-finance problems. You will build reusable financial ML frameworks, optimize training pipelines on HPC resources, integrate latency-sensitive models into production, and develop observable, high-performance ML systems using C, C++, Python, CUDA, and related GPU languages.
Responsibilities
- Apply state-of-the-art techniques to complex domains
- Build flexible and reusable frameworks for financial machine learning
- Optimize training pipelines using HPC resources
- Integrate machine-learning models into latency-sensitive production systems
- Develop large-scale observable and performant machine-learning systems
- Reduce research iteration cycle time
Requirements
- Publication record at leading machine-learning conferences or open-source AI research contributions
- Machine-learning and modern deep-learning knowledge
- Language-model architecture knowledge
- Python or C++ development
- PyTorch, JAX, or TensorFlow
- Quantitative problem-solving
- Communication with trading researchers