Campus AI Research Engineer – Deep Learning Intern

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