Software Engineer - Python

A leading quantitative trading firm is seeking a Software Engineer with excellent Python programming skills and experience supporting quantitative research and systematic trading . Working closely with traders and quantitative researchers, you'll build the systems, research tools, and signal pipelines that bring trading ideas into production.

The primary focus is writing efficient, production-quality Python for market data analysis, signal development, and research workflows , with an emphasis on performance, scalability, and reliability. Experience with both pandas and Polars is highly desirable , alongside the ability to optimize processing speed and memory usage across large financial datasets.

This opportunity is particularly well suited to engineers and quantitative developers from proprietary trading firms, hedge funds, or systematic investment teams who want close involvement in research and direct exposure to how their work supports live trading decisions.

What you'll do:

• Develop and optimize Python systems for processing and analyzing large-scale market and financial datasets.
• Build data, feature, and signal pipelines that support quantitative research and live trading.
• Partner with traders and quantitative researchers to translate research ideas into practical tools and production systems.
• Improve the speed, scalability, and memory efficiency of research workflows, enabling faster experimentation and iteration.
• Develop simulation and backtesting tools to evaluate trading ideas under realistic market conditions.
• Translate research prototypes into reliable production implementations.
• Investigate and resolve data quality, performance, and implementation issues across the research-to-production lifecycle.

Qualifications:

• 3–7 years of experience in software engineering or quantitative development, preferably within a proprietary trading firm, hedge fund, or systematic investment team
• Excellent Python coding skills , with a track record of building and maintaining production systems for research or trading.
• Strong proficiency in pandas and/or Polars, ideally with practical experience in both
• Hands-on experience handling large financial datasets , including efficient data manipulation, pipeline development, and performance optimization.
• Familiarity with quantitative research workflows, including signal development, experimentation, simulation, or backtesting.
• A strong foundation in probability, statistics, and time series analysis.
• An understanding of machine learning concepts, their application to systematic trading, and how models move from research into production.
• The ability to collaborate effectively with traders, quantitative researchers, and engineers to turn research objectives into robust technical solutions.

Experience with low-latency trading or other performance-critical systems is a plus.