We are seeking highly motivated and curious individuals to join our Machine Learning team . In this role, you will bridge the gap between advanced deep learning and financial markets, designing robust models for medium and high-frequency systematic trading strategies. You will manage the full ML lifecycle, from researching novel architectures to deploying scalable, low-latency models that directly drive trading revenue.

Key Responsibilities

• Feature Engineering: Analyze complex time-series data, orderbook dynamics and trade data to engineer high-signal features
• Deep Learning Architecture: Design and train deep-learning based models (MLP, LSTM, RNN, Transformers, RL agents, etc) tailored for financial trading environments
• Backtesting & Evaluation: Conduct comprehensive backtesting and simulation across various asset classes and exchanges; analyze trade execution and PnL attribution
• Model Deployment: Collaborate with engineering teams to optimize and deploy models into production
• MLOps & Automation: Build and maintain automated pipelines for data ingestion, model retraining, and continuous performance monitoring to streamline the research-to-production workflow

Qualifications

• Strong academic or professional foundation in machine learning, quantitative research, and/or other related STEM fields; open to both experienced candidates and highly-motivated fresh graduates
• Deep understanding of neural network architectures and their application to time-series forecasting
• Proficiency in Python and modern ML frameworks (PyTorch/TensorFlow/Jax); C++ preferred
• Solid command of probability theory, linear algebra and applied statistics
• Strong communication skills and able to articulate technical concepts with clarity
• High level of drive, curiosity and a passion for continuously learning in a fast-paced environment

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