Quantitative Engineer: Build Low-Latency Trading Systems

Conviction; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Conviction is seeking a Quantitative Engineer to work with the founders on building trading and backtesting systems that power Conviction. You will develop quantitative strategies, handle large datasets, and turn ideas into production-ready implementations.

You will focus on execution, backtesting, and strategy development across multiple markets, leveraging Python and time-series analysis to drive research and trading performance.

Qualifications

• Strong Python and quantitative programming skills.
• Experience building or researching systematic trading strategies.
• Strong understanding of statistics, probability, and quantitative methods.
• Experience working with large financial or time-series datasets.
• Experience designing and evaluating backtests.
• Understanding of common sources of backtest error, including look-ahead bias, survivorship bias, overfitting, transaction costs, and data leakage.
• Understanding of financial markets, order types, market structure, and trade execution.
• Experience with data analysis libraries and quantitative research tooling.

Responsibilities

• Develop trade-generation and execution logic for low-latency systems.
• Build and improve backtesting systems for historical performance.
• Develop production-ready, multi-agent trading strategies.
• Research and evaluate systematic trading strategies across equities, options, futures, and other financial markets.
• Work with large historical and real-time financial datasets (prices, fundamentals, news, alternative data).

Skills

Python

Quantitative programming

Time-series analysis

Statistics

Data analysis libraries

Tools

Pandas

NumPy

Backtesting libraries

Job description

Conviction is seeking a Quantitative Engineer to work with the founders on building trading and backtesting systems that power Conviction. You will develop quantitative strategies, handle large datasets, and turn ideas into production-ready implementations.

You will focus on execution, backtesting, and strategy development across multiple markets, leveraging Python and time-series analysis to drive research and trading performance.