Research Platform Engineer

Company Description

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: www.infquant.com
LinkedIn: linkedin.com/company/infinitequant

Job Description

This is a hands-on individual contributor role: you will own and extend our C++ simulation/backtesting framework across both digital assets and FX, exposing it to researchers via Python tooling. Your work ensures that strategy ideas move seamlessly from research → simulation → live trading, with accuracy and reliability. You will collaborate closely with traders, researchers, the Principal Engineer, and infra teams.

Key Responsibilities

• Simulation & Backtesting: Maintain and extend our C++ simulation/backtesting framework for both digital assets and FX, ensuring it faithfully reflects live exchange behavior.
• Matching Engine & Queuing: Implement and refine models of exchange matching engines and order queuing (FIFO, pro-rata, hidden orders, cancel/replace rules), ensuring execution simulations align with production fills.
• Python Integration: Build and maintain pybind11 bindings so researchers can interact with the C++ simulator from Python.
• Research Tooling: Develop Python scripts, data pipelines, and visualization tools that leverage the simulator for testing and analysis.
• Production Alignment: Guarantee that strategies tested in the simulator match production performance by debugging mismatches and validating data flows.
• Historical Data Handling: Work with large datasets stored on an NFS file system, building efficient indexing, access patterns, and preprocessing pipelines for researchers.
• Collaboration: Partner with researchers and traders to translate raw ideas into reproducible experiments.
• Reliability & Debugging: Investigate discrepancies between simulation, research, and production, and improve robustness of the research environment.
   

Location

• Dubai - United Arab Emirates

• New York City - United States

Qualifications