Role overview
Role Overview We are seeking an experienced Machine Learning Researcher to join our research team. This role requires expertise in designing and deploying deep learning models within high-performance, low-latency trading systems. You will be working on developing robust, scalable models and integrating them into our trading infrastructure. Responsibilities
Data Analysis & Preprocessing: Understand and preprocess orderbook data. Deep Learning Model Design: Design models for time-series and orderbook data (Transformers, RNNs, CNNs, Attention). Scalable Training Implementation: Implement parallelized data loading pipelines. Feature Engineering: Develop and optimize orderbook features using C++. Backtesting & Evaluation: Conduct rigorous backtesting across markets. Production Integration: Deploy models into real-time, low-latency systems.
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(e.g., KV caching, quantization, pruning) is advantageous. Open to both experience candidates and highly motivated fresh graduated.
Technical Skills
Deep Learning Architectures: Transformers, RNNs, CNNs, Attention mechanisms. 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.
Motivation & Learning
Passion for applying machine learning to quantitative finance. Drive to continuously improve models.
What we're looking for
Data Analysis & Preprocessing: Understand and preprocess orderbook data. Deep Learning Model Design: Design models for time-series and orderbook data (Transformers, RNNs, CNNs, Attention). Scalable Training Implementation: Implement parallelized data loading pipelines. Feature Engineering: Develop and optimize orderbook features using C++. Backtesting & Evaluation: Conduct rigorous backtesting across markets. Production Integration: Deploy models into real-time, low-latency systems.
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(e.g., KV caching, quantization, pruning) is advantageous. Open to both experience candidates and highly motivated fresh graduated.
Technical Skills
Deep Learning Architectures: Transformers, RNNs, CNNs, Attention mechanisms. 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.
Motivation & Learning
Passion for applying machine learning to quantitative finance. Drive to continuously improve models.