About this role
About Hellyeah AI
Hellyeah AI is building AI-powered solutions to help businesses make better decisions through advanced automation, data intelligence, and algorithmic systems.
We are looking for a Senior Quantitative Engineer / Algo Engineer to join our engineering team and build the next generation of quantitative strategy infrastructure. The ideal candidate has experience from top quantitative trading firms, hedge funds, or financial technology companies, with strong expertise in algorithm development, quantitative research infrastructure, and large-scale backtesting systems.
Responsibilities
Design, develop, and optimize quantitative trading strategies and algorithmic models
Build and maintain high-performance backtesting and simulation frameworks
Develop research infrastructure that enables rapid strategy iteration and validation
Collaborate with quantitative researchers to translate trading ideas into production-ready systems
Implement data pipelines, signal generation frameworks, and strategy evaluation tools
Improve the accuracy, scalability, and performance of simulation environments
Conduct strategy analysis, performance attribution, and model optimization
Ensure research results can be reliably transitioned into production systems
Requirements
5+ years of experience in quantitative development, algorithm engineering, or related fields
Experience working at leading quantitative trading firms, hedge funds, proprietary trading firms, or financial technology companies preferred
Strong experience building quantitative research platforms, backtesting engines, or trading infrastructure
Strong programming skills in Python and/or C++
Solid understanding of algorithmic trading, quantitative strategies, and financial data systems
Experience with large-scale time-series data processing and performance optimization
Strong problem-solving ability and engineering mindset
Preferred Qualifications
Experience with systematic trading strategies, factor models, or machine learning-based trading systems
Experience building low-latency or high-performance simulation systems
Background in mathematics, statistics, computer science, or related technical fields
Experience with market microstructure, execution models, or portfolio optimization