Quant Researcher

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.