Binance Accelerator Program - Quantitative Trading Strategy Algorithm

Binance; United States; A distancia; USD 60.000 - 90.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote-first

Competitive compensation

Career learning opportunities

Descripción de la vacante

Binance is building an AI-driven trading research program and seeks a Quantitative Trading Strategy Algorithm Intern. You will explore factor discovery, factor prediction, and trading system construction, using Python and ML/DL methods on multi-source data including market and on-chain data.

Ideal candidates are current Master’s or PhD students in CS/Math/Statistics/Financial Engineering/Physics, with strong quantitative foundations and commitment to stable weekly internship hours.

Formación

• Currently enrolled in a Master’s or PhD program with strong quantitative foundations.
• Interest in quantitative trading strategies and backtesting workflows.
• Experience with Python and ML/DL applied to finance data.

Responsabilidades

• Participate in discovery, construction, and validation of trading factors from market and on-chain data.
• Design and optimize factor prediction models using ML/DL techniques.
• Contribute to design, backtesting, and validation of trading strategies and risk research.
• Help build the quantitative trading strategy pipeline from data to backtesting.
• Track frontier methods bridging traditional markets and on-chain assets.

Conocimientos

Python

ML/DL methods

Quantitative trading

Time-series data

Factor mining

Educación

Master’s or PhD student

Fields: CS, Math, Statistics, Financial Eng, Physics

Herramientas

Python

Backtesting tools

Descripción del empleo

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

1. 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.

2. Participate in the design and optimization of factor prediction models, applying machine learning and deep learning methods to enhance signal predictive power and stability.

3. Participate in the design, backtesting, and validation of trading strategies, assisting with signal generation, portfolio construction, and risk control research.

4. Participate in building the quantitative trading strategy pipeline, helping to streamline the R&D workflow from data, factors, and models to backtesting.

5. 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

1. 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.

2. Strong interest in quantitative trading strategies, familiarity with factor mining and strategy backtesting workflows, and a basic understanding of strategy return and risk.

3. Proficient in Python, knowledgeable about ML/DL methods applied in quantitative scenarios, and experienced in handling financial time-series data.

4. 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).

5. Strong learning ability and research enthusiasm, high initiative, and ability to continuously explore in a fast-iterating environment.

Nice to Have

1. Course projects, competitions (e.g., quant competitions, Kaggle), or internship experience in quantitative research.

2. Exposure to quantitative research across both traditional finance and on-chain markets (DeFi, CEX, DEX).

3. Practical experience applying machine learning, reinforcement learning, or similar methods to financial data or trading scenarios.

4. 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.

By submitting a job application, you acknowledge that you have read our Candidate Privacy Notice.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing 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 judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.