Tech Stack

Description

You will design and implement scalable, production-grade platforms that support quantitative research and investment processes. Your work will focus on building quant tooling that enables researchers to efficiently develop, evaluate, and deploy quantitative models across investment processes. You'll collaborate with Research, Portfolio Management, and Data teams to create infrastructure that makes quantitative research repeatable, observable, and production-ready at scale.

Requirements

• Bachelor's degree or higher in Computer Science, Engineering, Applied Mathematics, or a related quantitative field
• 7+ years of experience in software engineering, with significant hands-on work supporting quantitative research, signal development, or systematic investing in production environments
• Demonstrated experience building and operating quantitative platforms that support signal modeling, evaluation, and deployment at scale
• Strong proficiency in Python and modern data processing and numerical computing tools
• Familiarity with machine learning and AI-assisted development techniques
• Solid understanding of system design concepts
• Demonstrated ability to deliver robust, transparent, and performant systems
• Strong attention to detail and a commitment to producing accurate, reliable, and well-engineered solutions
• Creativity and enthusiasm for solving complex problems
• Strong work ethic and a roll-up-your-sleeves mindset, with the ability to operate effectively in a hands-on, collaborative environment

Responsibilities

• Design and implement quant tooling that supports signal construction and evaluation workflows
• Lead the AI centric development and evolution of core platform capabilities
• Ensure platform flexibility while maintaining integration with downstream processes
• Incorporate machine learning and AI techniques into signal construction, parameter estimation, and efficacy evaluation workflows
• Provide architectural leadership to improve robustness, transparency, scalability, and performance
• Collaborate with data and platform teams to streamline access to market data, alternative datasets, and compute resources
• Contribute to the end-to-end machine learning lifecycle capabilities