AI-Driven Quantitative Trading Strategy Architect

Binance; United States; Hybrid; USD 150,000 - 230,000; Full time

Benefits offered by this job

Remote work options

Job summary

Binance is seeking an experienced quantitative researcher to build an AI-driven trading system across traditional and on-chain assets. You will own factor mining, model construction, backtesting, and live deployment, with a focus on generating sustainable alpha.

You will collaborate with engineering and data teams to optimize low-latency execution, manage risk, and scale the research platform for multi-market deployment.

Qualifications

• Advanced degree with strong quantitative foundation and programming proficiency.
• Experience in quantitative trading strategy R&D across factor mining, prediction, backtesting, and live deployment.
• Proficiency in Python for ML/DL on large-scale financial data.
• Familiarity with equities, futures, or crypto markets and trading costs, liquidity, slippage.

Responsibilities

• Factor mining, validation, and alpha signal development from multi-source data.
• Modeling and predicting signals using ML/DL methods while mitigating overfitting.
• Lead design, backtesting, and live validation of trading strategies including risk controls.
• Build end-to-end quantitative pipeline from data ingestion to live execution.
• Collaborate with engineering and data teams to ensure low-latency, stable deployment.
• Explore cross-market AI trading across traditional assets and on-chain markets.

Skills

Python

ML/DL experience

Quantitative research

Backtesting / signal validation

Education

Master’s degree in Computer Science / Mathematics / Statistics / Financial Engineering

Tools

Pandas

NumPy

Backtesting frameworks

Job description

Binance is seeking an experienced quantitative researcher to build an AI-driven trading system across traditional and on-chain assets. You will own factor mining, model construction, backtesting, and live deployment, with a focus on generating sustainable alpha.

You will collaborate with engineering and data teams to optimize low-latency execution, manage risk, and scale the research platform for multi-market deployment.