As a Senior Machine Learning Engineer (Trading & Financial Intelligence) , you will develop AI-powered systems and autonomous agents that transform how financial analysis and decision-making are conducted. You will build intelligent solutions that analyze markets, extract insights from complex financial data, and assist with risk management using advanced ML and quantitative techniques. This role offers the opportunity to apply cutting-edge AI, from traditional machine learning to modern LLM-based agents, to solve complex financial problems while collaborating with trading, research, and product teams.

WHAT YOU WILL BE DOING:

• Design and implement machine learning solutions for financial markets, ranging from predictive models to autonomous AI agents powered by LLMs
• Develop intelligent systems using both traditional ML approaches (time series analysis, anomaly detection, pattern recognition) and modern agentic frameworks (LangChain, reasoning loops, tool orchestration)
• Apply quantitative methods and data mining techniques to extract actionable insights from large-scale financial datasets
• Build end-to-end ML pipelines for model development, backtesting, and production deployment with robust monitoring and evaluation frameworks
• Create research platforms that enable rapid experimentation with both classical statistical models and LLM-based approaches for financial analysis
• Collaborate with traders, quants, and researchers to translate complex financial problems into scalable ML solutions
• Develop risk assessment and portfolio optimization systems using a combination of traditional quantitative methods and AI-driven approaches

WHAT YOU NEED TO BE SUCCESSFUL IN THIS ROLE:

• We welcome all applicants who are eligible to work in Singapore.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Financial Engineering, or related quantitative field
• 3+ years of experience in machine learning engineering, quantitative research, or data science with production systems
• Strong programming skills in Python with expertise in scientific computing libraries (pandas, numpy, scikit-learn) and ML frameworks
• Experience with diverse ML techniques including supervised/unsupervised learning, deep learning, time series forecasting, and statistical modeling
• Familiarity with Large Language Models and modern AI techniques, including prompt engineering, fine-tuning, and agentic systems is highly valued
• Strong quantitative and analytical skills with ability to apply mathematical and statistical concepts to real-world problems
• Experience with data mining and feature engineering from large, complex datasets
• Problem-solving mindset with the ability to work independently and in fast-paced, results-oriented environments
• Good communication skills to present technical findings to both technical and non-technical stakeholders
• Knowledge of financial markets, trading systems, or quantitative finance is a plus but not required - we value strong technical skills and learning ability
• Experience with backtesting frameworks, risk modeling, or portfolio optimization is beneficial