Quantitative Research Internship

Location: New York, NY (In-Person, 5 Days/Week)
Company: Blockhouse
Job Type: Full-Time Internship (Start Immediately, Through December)

Blockhouse is building intelligent execution algorithms for institutional traders. Our platform combines market microstructure theory, mathematical modeling, and machine learning to minimize slippage and enhance execution quality across equities and fixed income markets.

We’re looking for a Quantitative Research Intern with a strong foundation in mathematical reasoning and a deep interest in applying first-principles thinking to trading execution. This is a hands-on research role working directly with our production trading infrastructure. We are seeking candidates who are available to start immediately and work full-time through December, with a strong preference for in-person work in our NYC office 5 days per week.

• Design and validate execution signals rooted in market structure theory - such as order flow imbalance (OFI), cross-impact, and liquidity metrics.- across multiple order book depths and time horizons.

• Decompose trading problems into their mathematical components, defining objective functions, constraints, and solution frameworks for execution strategies.

• Apply causal inference and statistical diagnostics to test predictive validity of features across volatility regimes and structural breaks.

• Use Python to develop modular research pipelines for feature extraction, backtesting, and simulation of execution strategies.

• Collaborate with senior researchers to evaluate algorithm performance under different market conditions and compare outcomes to industry-standard benchmarks like TWAP and VWAP.

• Contribute to rigorous internal research documentation, including derivations, assumptions, and version-tracked model updates.

• Currently pursuing or recently completed a Bachelor’s or Master’s degree in Applied Math, Statistics, Computer Science, Financial Engineering, or a related quantitative discipline.

• Demonstrated proficiency in mathematical modeling - including optimization, stochastic processes, or statistical inference - and the ability to break down complex systems from first principles.

• Strong programming skills in Python, especially with numerical and data libraries such as NumPy, Pandas, SciPy, and Matplotlib.

• Experience reading and implementing models from academic papers, including clear articulation of underlying assumptions, input parameters, and mathematical structure.

• Familiarity with market microstructure, order book mechanics, and the inherent noise in high-frequency financial data.

• Prior exposure to causal inference, signal processing, or real-time systems is a plus.

• Highly analytical, self-directed, and capable of formulating precise questions to drive research forward.

• Available to start immediately and commit full-time (Monday–Friday in-person) through December 2025.

• Direct exposure to live trading systems and algorithms used by institutional clients.

• Opportunity to work on research that spans first-principles modeling, real-time systems, and empirical testing.

• Close collaboration with experienced quant researchers and infrastructure engineers.

• High-ownership, fast-paced environment with room to contribute meaningfully from day one.

• Based in NYC with a strong in-person research culture.

Compensation: Cash plus equity-based compensation depending on experience and hours committed. Total compensation ranges from $25 to $40 per hour based on experience and contributions.

Blockhouse supports CPT/OPT, is e-verified, and accommodates flexible international payment arrangements.