Overview
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
Interested candidates, please click the link to apply:
#J-18808-Ljbffr