Machine Learning Researcher

About the role

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.

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Company Intel

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