Machine Learning Researcher
Established in 2018, Kronos Research is a quantitative trading firm that has become a leader in the industry, handling billions of dollars in daily transactions. They specialize in market making for both tokens and exchanges, proprietary trading, asset management, and venture capital investments within the crypto space.
About Kronos Research
Established in 2018, Kronos Research is a quantitative trading firm that handles billions of dollars in daily transactions. The company specializes in several areas including Token Market Making, where it partners with top crypto projects to provide liquidity; Exchange Market Making, using high-frequency and quantitative strategies to enhance liquidity and limit volatility; Proprietary Quant Trading, leveraging advanced infrastructure and research; Asset Management, offering returns through a multi-strategy approach; and Venture Investment, investing in and advising early-stage founders. Kronos Research works with numerous centralized and decentralized exchanges such as Binance, OKX, Kucoin, Uniswap, and Coinbase.
Skills
About the 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.
Requirements
- 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
Responsibilities
- Analyze complex time-series data, orderbook dynamics and trade data to engineer high-signal features
- Design and train deep-learning based models (MLP, LSTM, RNN, Transformers, RL agents, etc) tailored for financial trading environments
- Conduct comprehensive backtesting and simulation across various asset classes and exchanges; analyze trade execution and PnL attribution
- Collaborate with engineering teams to optimize and deploy models into production
- Build and maintain automated pipelines for data ingestion, model retraining, and continuous performance monitoring