Senior Research Platform Engineer

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 Strong proficiency in C++, with hands-on experience maintaining or extending large, performance-critical systems. Experience with pybind11 or equivalent Python–C++ integration frameworks. Solid Python skills for research pipelines, data analysis, and scripting. Prior work with distributed simulation or backtesting frameworks in HFT. Deep understanding of matching engines, order book mechanics, queuing models, and special order types (FIFO, pro-rata, priority). Experience working with large datasets stored in a distributed file system — building indexing, parsing, and efficient access pipelines. Familiarity with databases, caching, and messaging systems (SQL, Redis, Kafka) is a plus. Strong debugging skills across both Python and C++. Startup or small-team background with high ownership. Familiarity with trading concepts (PnL, risk, market data, order types) InfiniteQuant is an Equal Employment Opportunity employer. We are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants without regard to race, color, religion, sex, pregnancy, national origin, age, disability, marital status, sexual orientation, gender identity, genetic information, military and veteran status, and any other characteristics protected by applicable law. We seek to recruit, develop, and retain the most talented and qualified applicants from a diverse candidate pool. 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: