AI-Driven Quant Trading Strategy Engineer

Binance; Sydney; Hybrid; AUD 180,000 - 260,000; Full time

Benefits offered by this job

Work-from-home

Competitive salary

Career growth

Continuous learning

Flat structure

Job summary

Binance is building an AI-driven trading system spanning traditional assets and on-chain assets. We seek algorithmic researchers with deep trading knowledge to participate in factor mining, prediction, strategy construction, and system integration.

You will own factor development, model training, backtesting, and live deployment, collaborating with data and engineering teams to ensure low-latency, scalable execution and robust risk controls.

Qualifications

• Master's degree or higher in a quantitative field with strong programming skills.
• Proven experience in R&D of quantitative trading strategies across data-to-live deployment.
• Proficient in Python and applying ML/DL to time-series data.
• Knowledge of markets (equities, futures, crypto) and execution considerations.
• Experience building end-to-end strategy pipelines from data to live trading.
• Strong research capability and results-driven mindset.

Responsibilities

• Mine and validate trading factors from multi-source data.
• Design and optimize models to improve signal accuracy and reduce overfitting.
• Lead backtesting and live deployment validation of strategies.
• Build end-to-end quantitative trading pipelines.
• Collaborate with engineering to ensure low-latency, robust deployment.
• Explore cross-market AI trading across traditional and on-chain assets.

Skills

Python

Quantitative trading

Machine learning

Time-series analysis

End-to-end pipeline

Education

Master's degree or higher in CS, Math, Stats, Financial Engineering, Physics, or related fields

Tools

TensorFlow/PyTorch

Pandas

Job description

Binance is building an AI-driven trading system spanning traditional assets and on-chain assets. We seek algorithmic researchers with deep trading knowledge to participate in factor mining, prediction, strategy construction, and system integration.

You will own factor development, model training, backtesting, and live deployment, collaborating with data and engineering teams to ensure low-latency, scalable execution and robust risk controls.