Binance
via Lever
Binance Accelerator Program - Quantitative Trading Strategy Algorithm
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 13h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 14h ago
The date the source published, not the day we noticed it (2026-09-17). Last seen at its source 1h ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Hong Kong, Taiwan, Australia
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay A$1,775–2,362/mo
Middle 50% of 30 listings that do state pay — Finance · all levels · APAC · AUD/month. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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lever employer's own board first seen 13h ago · last seen 1h ago
The listing
About the Role
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.