Binance
via Lever
Quantitative Trading Strategy Algorithm Engineer
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 just now — 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 1h ago
The date the source published, not the day we noticed it (2026-09-16). Last seen at its source just now.
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
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay A$1,750–2,250/mo
Middle 50% of 29 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 just now · last seen just now
The listing
About the Role
Responsibilities
- Factor Mining & Validation: Discover, construct, and validate trading factors from multi-source data including market data, fundamental data, and on-chain data. Continuously iterate the factor library to identify effective alpha signals.
- Factor Prediction Modeling: Design and optimize prediction models using machine learning and deep learning methods to improve signal accuracy and stability while controlling overfitting and strategy decay.
- Strategy Design & Backtesting: Lead the design, backtesting, and live deployment validation of trading strategies — covering signal generation, portfolio construction, risk control, and execution optimization. Take ownership of strategy P&L and risk performance.
- Quant Strategy Pipeline Development: Build and refine the end-to-end quantitative trading strategy pipeline — from data ingestion, factor computation, model prediction, backtesting through to live execution — improving research efficiency, deployability, and reproducibility.
- Trading System Integration: Collaborate with engineering and data teams to solve technical challenges including data connectivity, low-latency execution, and strategy deployment, ensuring stable strategy operation in production.
- Cross-Market AI Trading: Explore the adaptation and implementation of AI-driven trading across both traditional financial markets (equities, futures) and on-chain asset markets, leveraging the unique characteristics of each.
Requirements
- Master's degree or above in Computer Science, Mathematics, Statistics, Financial Engineering, Physics, or related fields, with a solid quantitative foundation and programming proficiency.
- Proven experience in quantitative trading strategy R&D, familiar with the full workflow of factor mining, factor prediction, strategy backtesting, and live deployment. Deep understanding of strategy P&L, risk, and alpha decay.
- Proficient in Python, with hands-on experience applying ML/DL methods in quantitative scenarios and processing large-scale financial time-series data.
- Familiarity with trading mechanisms and data characteristics of at least one market (equities, futures, or other traditional financial markets; or cryptocurrency / on-chain assets). Understanding of real-world factors such as trading costs, liquidity, and execution slippage.
- Experience building a complete strategy pipeline or quantitative research platform, with the ability to independently deliver an end-to-end strategy loop from data to live trading.
- Strong research capability and results-driven mindset, with the ability to continuously optimize strategy performance in a fast-iteration environment.
Bonus Qualifications
- Track record of managing capital at scale in live trading or generating sustained alpha.
- Cross-market quantitative experience spanning both traditional finance and on-chain markets (DeFi, CEX, DEX).
- Familiarity with high-frequency trading, market-making strategies, or cross-market arbitrage.
- Practical experience applying frontier AI methods (large language models, reinforcement learning) to trading strategies.