Description

A leading quantitative investment firm is seeking an experienced software engineer to build and enhance the technology stack that supports systematic research and trading. The team develops platforms and tools used by quantitative researchers and portfolio managers to generate, test, and deploy investment strategies at scale.

This is a highly collaborative role working at the intersection of software engineering and quantitative investing. Engineers own projects end-to-end, from architecture and development through production deployment and operational support.

Key Responsibilities

• Build and maintain software supporting quantitative research, data processing, model development, and automated trading workflows.
• Develop Python-based libraries, tools, and frameworks that improve research productivity and scalability.
• Design and optimize C++ applications for performance-critical production systems.
• Partner closely with researchers and investment professionals to translate complex business requirements into robust technical solutions.
• Improve system reliability, observability, testing, and operational excellence.
• Troubleshoot challenging technical issues across research and production environments.
• Contribute to engineering best practices through design reviews, testing, and mentorship.

Ideal Background

• 5+ years of professional software engineering experience supporting production systems.
• Strong expertise in Python, C++, or both.
• Excellent software engineering fundamentals, including architecture, debugging, performance optimization, and maintainability.
• Experience building data-intensive, distributed, or mission-critical applications.
• Strong problem-solving ability, ownership mentality, and willingness to learn new domains.
• Effective communication skills and experience working with cross-functional technical stakeholders.
• Degree in Computer Science, Engineering, Mathematics, Physics, Statistics, or a related field (or equivalent experience).

Preferred Qualifications

• Exposure to quantitative finance, systematic investing, financial markets, or market data.
• Experience with Linux, cloud infrastructure, distributed systems, GPUs, data platforms, or low-latency applications.
• Familiarity with machine learning, numerical computing, simulation, optimization, or large-scale data processing.