Algorithmic Trader / Quantitative Trader

Role Research, design, and deploy new algorithmic trading strategies for digital assets, forex, or commodities markets Take full responsibility for the strategy lifecycle, from idea generation and research to simulation and live production Iterate on strategy behavior and performance in live markets Build a deep understanding of market microstructure through hands-on production trading Program Structure Designed for experienced traders with an existing strategy or experienced researchers who have not yet scaled a fully independent strategy Provides a structured path toward independent strategy ownership and progression to Trading Pod Lead Includes training, technical review, and ongoing advisory support from senior traders and system owners We Provide In-house data, simulation, and low-latency trading platforms Proprietary trading infrastructure and internal capital Direct market access to global exchanges with institutional connectivity Training, mentorship, and advisory support from senior traders Location Dubai - United Arab Emirates New York City - United States Master or Ph.D. degree in STEM (Science, Technology, Engineering, and Mathematics) Prior work experience in high frequency trading firms or hedge funds Prior experience developing or operating fully automated quantitative trading strategies C++ / Python experience Solid understanding of market microstructure and execution High-ownership, self-directed, systems-oriented mindset InfiniteQuant is a global quantitative trading and technology company. As a privately owned proprietary trading firm, we research, develop, and deploy high-frequency quantitative trading strategies across global financial markets. Our entire technology stack, from market data infrastructure and research platforms to simulation engines, execution systems, and trading strategies, is built in-house. Our high-frequency trading strategies generate hundreds of millions of order messages daily across thousands of symbols on major electronic exchanges. We maintain a multi-year archive of historical tick-by-tick market data, powering quantitative research, simulation, and strategy development. Website: LinkedIn: