Job Description
Location: New York, NY (Metro Area) About the Opportunity Join a pioneering investment firm at the intersection of advanced machine learning and systematic trading. We have developed a proprietary, automated "Alpha Factory" that is already live-trading, and we are now seeking a visionary Quantitative Researcher to serve as the foundational architect for our strategy discovery engine. In this high-impact role, you will act as a "Player-Coach" for our technology—simultaneously developing high-sharpe signals while systematically codifying your research intuition into our autonomous AI infrastructure. Responsibilities Systemic Mentorship: Guide the evolution of a self-learning trading engine by translating complex quantitative intuition into automated research workflows. Alpha Generation: Design, backtest, and deploy innovative predictive signals across diverse asset classes to drive firm P&L. Infrastructure Optimization: Collaborate with core engineering teams to refine the "Alpha Factory," ensuring the system can autonomously identify, validate, and execute new trading ideas. Feedback Integration: Analyze system performance and "teach" the AI to recognize and avoid false signals, improving the overall autonomy of the discovery pipeline. Strategic Leadership: Serve as a subject matter expert on market microstructure and quantitative modeling to steer the firm’s long-term research roadmap. Requirements Proven Track Record: Extensive experience in systematic alpha research within a hedge fund or high-frequency trading environment. Expert Programming: Advanced proficiency in Python, C++, or similar languages, specifically applied to large-scale data analysis and modeling. Mathematical Excellence: Deep understanding of statistics, machine learning, and financial econometrics. Architectural Mindset: Experience not just in finding signals, but in building the frameworks and tools that facilitate signal discovery at scale. Academic Background: Advanced degree (Master’s or PhD) in a quantitative field such as Physics, Mathematics, Computer Science, or Engineering. Preferred Qualifications Experience with reinforcement learning or LLMs applied to financial time-series data. Prior experience at a "founding" or early-stage systematic firm. Knowledge of cloud-native high-performance computing (HPC) environments. Compensation & Benefits Highly competitive base salary and performance-based bonus. Significant equity/founding member participation. Comprehensive health, dental, and vision insurance. Professional development budget and flexible work arrangements.