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.