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Deep Learning Trader
3 weeks ago
We are seeking a highly skilled Quantitative Finance Researcher to join our research team. This role requires expertise in designing and deploying deep learning models within high-performance, low-latency trading systems.
Responsibilities
- Data Analysis & Preprocessing: Understand and preprocess orderbook data to gain valuable insights into market trends.
- Deep Learning Model Design: Design models for time-series and orderbook data using various techniques such as Transformers, RNNs, CNNs, and Attention.
- Scalable Training Implementation: Implement parallelized data loading pipelines to improve training efficiency.
- Feature Engineering: Develop and optimize orderbook features using C++ to enhance model performance.
- Production Integration: Deploy models into real-time, low-latency systems to ensure seamless integration with trading infrastructure.
Requirements
- Background in machine learning or quantitative research, preferably related to financial markets.
- Experience deploying ML models in real-time, low-latency environments is a plus.
- Familiarity with optimizing model latency and inference speed is advantageous.
Technical Skills
- Programming Languages: Python, C++, Jax/PyTorch.
- Model Optimization: Optimizing models for high-performance trading systems.
Analytical & Communication Skills
- Strong mathematical and statistical background (probability theory, linear algebra, calculus).
- Ability to articulate complex technical concepts.
- Passion for applying machine learning to quantitative finance.
- Drive to continuously improve models.
