Research Engineer

Founding Quant Research Platform Engineer Compensation: $150,000–$250,000 Base Salary + Equity We are an early-stage quantitative investment firm building an AI-powered research system that transforms investment ideas into reliable implementations and actionable evidence—while learning from every experiment. We are building the next generation of systematic investment technology and are looking for an exceptional engineer to join our founding team. You will build the quantitative research platform powering our AI-driven investment research system. The system implements and tests investment signals, while you will develop and own the infrastructure it depends on—including the backtesting engine, financial-data pipelines, evaluation tools, and the pathway from research to live trading. You will work directly with quantitative researchers and engineers to determine what the platform should do and deliver working systems without waiting for perfect specifications. Because the platform runs experiments continuously, the backtesting system must serve as a trusted referee: fast, accurate, reproducible, and extremely difficult to fool. As an early member of the team, you will have a meaningful impact on the company's technology, product strategy, engineering standards, and culture. Partner with researchers and engineers to build the platform that transforms investment ideas into testable signals, including signal frameworks, reusable feature and model components, and experiment-tracking systems. Build and own the backtesting engine and research APIs used by researchers, engineers, and AI agents. Build and operate data pipelines for large financial datasets. Ingest and validate complex vendor feeds while maintaining accurate point-in-time data. Properly account for corporate actions, delistings, and security identifiers across U.S. equities. Build signal diagnostics, risk-attribution tools, statistical tests, and automated controls that detect look-ahead bias, data leakage, and errors in AI-generated code. Deploy research strategies and systems into production and take ownership of monitoring and ongoing performance. Reconcile live trading results against backtested expectations in an environment where errors can have meaningful financial consequences. Profile and optimize computationally intensive numerical workloads so the research cycle takes minutes rather than hours. At least two years of experience building quantitative research or trading systems at a quantitative fund, proprietary trading firm, systematic investment team, or a comparable environment. Expert-level Python skills and deep familiarity with numerical and data-processing libraries such as NumPy, pandas, Polars, and SciPy. Strong software-engineering fundamentals, including testing, debugging, profiling, and building maintainable systems that others can extend. Experience developing pipelines for large financial datasets, preferably involving U.S. equities, vendor feeds, point-in-time fundamentals, corporate actions, and security symbology. Hands-on experience building or maintaining a backtesting platform. Strong understanding of the systematic investment lifecycle, including signal generation, risk modeling, portfolio construction, execution, and transaction costs. A solid foundation in statistics and a practical understanding of machine learning. Experience partnering directly with quantitative researchers and engineers to build reliable research tools. Meaningful equity participation Company-subsidized medical, dental, and vision insurance This is an opportunity to join a well-funded, early-stage quantitative investment firm whose founding team brings deep experience across machine learning, quantitative finance, investment management, and hedge-fund technology. You will help build the core research and trading infrastructure behind an AI-native systematic investment platform—and directly influence how the firm develops from its earliest stage.