Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.
About the Role
We are building an AI-driven trading system that covers traditional financial assets (equities, etc.) and on-chain assets. We are seeking algorithmic researchers with deep understanding of trading strategies to participate in the full lifecycle — from factor mining and prediction to strategy construction and system integration — combining quantitative research expertise with AI technology to build a trading strategy system that generates sustainable alpha.
Responsibilities
- Factor Mining & Validation: Discover, construct, and validate trading factors from multi-source data including market data, fundamental data, and on-chain data. Continuously iterate the factor library to identify effective alpha signals.
- Factor Prediction Modeling: Design and optimize prediction models using machine learning and deep learning methods to improve signal accuracy and stability while controlling overfitting and strategy decay.
- Strategy Design & Backtesting: Lead the design, backtesting, and live deployment validation of trading strategies — covering signal generation, portfolio construction, risk control, and execution optimization. Take ownership of strategy P&L and risk performance.
- Quant Strategy Pipeline Development: Build and refine the end-to-end quantitative trading strategy pipeline — from data ingestion, factor computation, model prediction, backtesting through to live execution — improving research efficiency, deployability, and reproducibility.
- Trading System Integration: Collaborate with engineering and data teams to solve technical challenges including data connectivity, low-latency execution, and strategy deployment, ensuring stable strategy operation in production.
- Cross-Market AI Trading: Explore the adaptation and implementation of AI-driven trading across both traditional financial markets (equities, futures) and on-chain asset markets, leveraging the unique characteristics of each.
Requirements
- Master's degree or above in Computer Science, Mathematics, Statistics, Financial Engineering, Physics, or related fields, with a solid quantitative foundation and programming proficiency.
- Proven experience in quantitative trading strategy R&D, familiar with the full workflow of factor mining, factor prediction, strategy backtesting, and live deployment. Deep understanding of strategy P&L, risk, and alpha decay.
- Proficient in Python, with hands-on experience applying ML/DL methods in quantitative scenarios and processing large-scale financial time-series data.
- Familiarity with trading mechanisms and data characteristics of at least one market (equities, futures, or other traditional financial markets; or cryptocurrency / on-chain assets). Understanding of real-world factors such as trading costs, liquidity, and execution slippage.
- Experience building a complete strategy pipeline or quantitative research platform, with the ability to independently deliver an end-to-end strategy loop from data to live trading.
- Strong research capability and results-driven mindset, with the ability to continuously optimize strategy performance in a fast-iteration environment.
Bonus Qualifications
- Track record of managing capital at scale in live trading or generating sustained alpha.
- Cross-market quantitative experience spanning both traditional finance and on-chain markets (DeFi, CEX, DEX).
- Familiarity with high-frequency trading, market-making strategies, or cross-market arbitrage.
- Practical experience applying frontier AI methods (large language models, reinforcement learning) to trading strategies.
