Role Overview
We are seeking highly motivated and curious individuals to join our Machine Learning team at Kronos Research. 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