Quant
Quantitative Researcher _MFT/HFT Trading
Job ID: #
We are a cutting-edge AI-powered trading technology firm that merges machine learning with quantitative finance to deliver next-generation trading solutions. Our team consists of elite researchers, engineers, and data scientists from top global institutions (Tsinghua, NUS, NTU, Peking University, HKUST, and more), alongside industry veterans from leading tech and finance firms.
We specialize in AI-driven trading strategies, providing institutional-grade solutions to major brokerages and financial organizations. Our mission is to revolutionize quantitative investing by integrating deep learning, large language models (LLMs), and high-performance trading infrastructure.
Your Role: AI & Quantitative Strategy Development
As a key member of our AI/ML research team, you will:
Develop next-gen trading strategies using large language models (LLMs) and foundational AI techniques.
Build and optimize AI-powered quantitative research pipelines—from data processing to strategy backtesting.
Enhance trading performance by applying machine learning (ML), NLP, and deep learning to financial markets.
Analyze strategy performance, identify inefficiencies, and implement AI-driven improvements.
Collaborate with quant researchers & engineers to deploy scalable, low-latency trading solutions.
Industry
Location:
Singapore/Hongkong/Shanghai/Beijing/Shenzhen
Company Size:
Job Type:
Full Time
Date:
Requirements
Technical Expertise
Strong programming skills – Python (essential), with experience in AI/ML frameworks (PyTorch, TensorFlow, Hugging Face).
Experience with LLMs – Fine-tuning, prompt engineering, or deploying models like GPT, Transformer, or Llama in real-world applications.
Quantitative & statistical knowledge – Asset pricing, factor models, time-series analysis, and portfolio optimization.
Machine learning fundamentals – Ability to design, train, and optimize ML models for financial data.
Preferred Background
Degree in Computer Science, Finance, Statistics, Applied Math, or related quantitative fields (Bachelor’s/Master’s/PhD).
Prior exposure to quant trading, algorithmic finance, or AI-driven investment strategies.
Familiarity with financial datasets (market data, alternative data, or unstructured text for sentiment analysis).