Binance Accelerator Program - Quantitative Trading Strategy Algorithm

Binance; Australia; Remote; AUD 17,000 - 26,000; Full time

Benefits offered by this job

Remote-first

Mentorship program

Career growth opportunities

Job summary

Binance is seeking a Quantitative Trading Strategy Algorithm Intern to join our AI-driven research team in Australia. You will explore factor discovery, model development, and backtesting within a fast-paced, data-driven environment.

You are a master's or PhD student in a quantitative field, proficient in Python, and eager to apply ML/DL methods to financial time-series data across traditional and on-chain markets. Remote-friendly internship with mentoring and growth opportunities.

Qualifications

• Master's or PhD candidate in a quantitative field with strong programming skills.
• Strong interest in quantitative trading strategies and backtesting workflows.
• Proficient in Python and ML/DL methods for quantitative finance.
• Experience with financial time-series data.
• Understanding of trading mechanisms in traditional or crypto markets.

Responsibilities

• Discover, construct, and validate trading factors from multiple data sources.
• Design and optimize factor prediction models using ML/DL techniques.
• Design, backtest, and validate trading strategies; assist with risk research.
• Build the quantitative trading pipeline from data to backtesting.
• Track frontier AI-driven trading methods across markets and on-chain assets.

Skills

Python

ML/DL

Time-series data

Quantitative trading

Research mindset

Education

Master's or PhD student in quantitative field

Tools

Pandas

NumPy

Backtesting

Job description

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, and more. Our mission is to increase the freedom of money and financial access for people around the world.

About the Role

We are building an AI-driven trading system covering traditional financial assets such as equities as well as on-chain assets. We are looking for a Quantitative Trading Strategy Algorithm Intern who is passionate about trading strategies to participate in research spanning factor discovery, factor prediction, and trading strategy and system construction — combining quantitative research capabilities with AI technology to grow rapidly through real-world strategy R&D.

Responsibilities

• - Participate in the discovery, construction, and validation of trading factors, exploring effective alpha signals from multi-source data including market data, fundamental data, and on-chain data.
• - Participate in the design and optimization of factor prediction models, applying machine learning and deep learning methods to enhance signal predictive power and stability.
• - Participate in the design, backtesting, and validation of trading strategies, assisting with signal generation, portfolio construction, and risk control research.
• - Participate in building the quantitative trading strategy pipeline, helping to streamline the R&D workflow from data, factors, and models to backtesting.
• - Track frontier methods in quantitative and AI-driven trading, conducting exploratory research that combines the market characteristics of traditional equities and on-chain assets.

Requirements

• - Current Master's or PhD student in Computer Science, Mathematics, Statistics, Financial Engineering, Physics, or a related field, with a strong quantitative foundation and programming skills, able to commit to stable weekly internship hours.
• - Strong interest in quantitative trading strategies, familiarity with factor mining and strategy backtesting workflows, and a basic understanding of strategy return and risk.
• - Proficient in Python, knowledgeable about ML/DL methods applied in quantitative scenarios, and experienced in handling financial time-series data.
• - Understanding of trading mechanisms and data characteristics in at least one market (equities, futures, or other traditional financial markets; or crypto and on-chain assets).
• - Strong learning ability and research enthusiasm, high initiative, and ability to continuously explore in a fast-iterating environment.

Nice to Have

• - Course projects, competitions (e.g., quant competitions, Kaggle), or internship experience in quantitative research.
• - Exposure to quantitative research across both traditional finance and on-chain markets (DeFi, CEX, DEX).
• - Practical experience applying machine learning, reinforcement learning, or similar methods to financial data or trading scenarios.
• - Publications, open-source projects, or personal research outcomes in finance or mathematical modeling.

Why Binance

• - Shape the future of finance
• - Collaborate with world-class talent in a user-centric global organization with a flat structure
• - Tackle challenging projects in a fast-paced and innovative enviornment with autonomy
• - Thrive in a results-driven workplace with opportunities for career growth and continuous learning
• - Competitive salary and company benefits
• - Remote-first, work-from-home arrangement (may vary depending on the nature of the role and team)

Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analysing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgement. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.