AQR

2027 Research and Portfolio Management Engineering Summer Analyst

Greenwich, CT

Sponsorship not specifiedDetected 1 day ago

PythonObject-Oriented ProgrammingAWSResearchCommunicationCollaboration

About the role

AQR is a global investment firm built at the intersection of financial theory and practical application. We strive to deliver concrete, long-term results by looking past market noise to identify and isolate the factors that matter most, and by developing ideas that stand up to rigorous testing. By putting theory into practice, we have become a leader in
alternative strategies and an innovator in traditional portfolio management since 1998.

We recognize and respect the power of collaboration, and believe transparency and openness to new ideas leads to innovation.
You will also help to maintain a sophisticated infrastructure and tooling environment in support of the business, as well as a complex production process including orchestrating numerous workflows with large amounts of data.
As a software engineer at AQR, you will build or extend our:
Ability and desire to develop in Python (our primary language for the business)
Ability and desire to develop in Java.
Understanding of design patterns and object-oriented programming techniques

Data ingestion and validation engine
Portfolio Rebalancing and Order Generation Platform and Services
Scalable, AWS-based storage and computing infrastructure
Strong attention to detail, passion for careful testing
Understanding or experience in finance a plus
Finishing either a Bachelor's or Master's degree program between December 2027 and June 2028
Mature and thoughtful, with the ability to operate within a collaborative, team-oriented culture
Hard working and eager to learn in a highly intellectual, innovative environment

We strive to deliver concrete, long-term results by looking past market noise to identify and isolate the factors that matter most, and by developing ideas that stand up to rigorous testing.

This listing is sourced directly from AQR's careers page and normalized into a canonical job model.