Senior Algorithm Engineer – Quantitative AI Systems

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