About this role

▸about Binance — company background

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.

We are building out a new research function at the intersection of artificial intelligence and quantitative trading to improve the efficiency of execution algo models and more, and we are looking for a Junior Quantitative Researcher to be a founding member of this effort. You will work alongside senior quants, engineers, and traders to design AI-driven workflows that generate alpha signals, diagnose model and PnL behavior, and deepen our understanding of market microstructure.

This is a high-ownership role suited to someone who is genuinely excited about markets, has a strong research background, and is already building with modern AI tooling — including LLM-based agents. We are open to hiring at the fresh-PhD level, provided you can demonstrate research depth and a real interest in trading.

Responsibilities

• Signal research and construction. Develop, test, and productionize predictive signals across asset classes using a combination of statistical methods, machine learning, and AI agent–driven research workflows. Take ideas from hypothesis through backtest, validation, and deployment.

• Root cause analysis (RCA). Investigate model behavior, signal decay, PnL attribution, and unexpected trading outcomes. Build tools — including agentic ones — that accelerate diagnosis and shorten the loop between observation and fix.

• Market microstructure research. Study order book dynamics, execution costs, liquidity, and venue behavior to inform both signal design and execution strategy.

• AI agent infrastructure for research. Help design and extend internal agentic systems that automate parts of the research pipeline — data exploration, hypothesis generation, backtest configuration, results summarization, and report drafting.

• Collaborate broadly. Work closely with traders, engineers, and other researchers to turn ideas into live, monitored strategies.