Quantitative Trading Strategy Algorithm Engineer

As a Quantitative Trading Strategy Algorithm Engineer at Binance, you build an AI-driven trading system covering traditional financial assets and on-chain assets. You combine quantitative research expertise with AI technology to discover, validate, and deploy trading strategies that generate sustainable alpha across equities, futures, cryptocurrencies, and blockchain-based markets.

What you'll do

• Discover, construct, and validate trading factors from multi-source data including market data, fundamental data, and on-chain data, continuously iterating the factor library to identify effective alpha signals.
• Design and optimize prediction models using machine learning and deep learning methods to improve signal accuracy and stability while controlling overfitting and strategy decay.
• 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.
• Build and refine the end-to-end quantitative trading strategy pipeline from data ingestion, factor computation, and model prediction through backtesting to live execution, improving research efficiency, deployability, and reproducibility.
• 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.
• 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.

What you bring

• A Master's degree or above in Computer Science, Mathematics, Statistics, Financial Engineering, Physics, or a related field 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.
• Proficiency in Python with hands-on experience applying machine learning and deep learning 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 and on-chain assets), including 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.

Nice to have

• A 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 such as large language models or reinforcement learning to trading strategies.

What we offer

• Competitive salary and company benefits
• Work-from-home arrangement (may vary depending on the work nature of the business team)
• Opportunity to work with world-class talent in a user-centric global organization with a flat structure
• Autonomy to tackle unique projects in an innovative environment
• Career growth and continuous learning opportunities in a results-driven workplace

About Binance

Binance is a leading global blockchain ecosystem behind the world's largest cryptocurrency exchange by trading volume and registered users, trusted by 300+ million people in 100+ countries. The platform offers trading and finance, education, research, payments, institutional services, Web3 features, and more, leveraging the power of digital assets and blockchain to build an inclusive financial ecosystem.