About AQR Capital Management

Founded in 1998, AQR is a global systematic investment management firm. As quantitative investors, research is the foundation of everything we do. We manage a range of alternative and long-only strategies designed to meet the needs of institutional and wealth investors around the world.

AQR’s culture reflects the firm’s academic roots and entrepreneurial spirit. Our collaborative environment fuels innovation and encourages employees to ask thoughtful questions, think creatively and bring new ideas forward. Continuous learning is an important part of the employee experience, helping our people build skills and grow throughout their careers. Employees at every level have opportunities to contribute, regardless of title or tenure.

The firm has 800 employees with headquarters in Greenwich, Connecticut and offices in London, Frankfurt, Dubai, Hong Kong, Sydney and Bengaluru.

The Team

AQR’s Research Engineering team is seeking a strong technical engineer to strengthen our business initiatives in QRD, focused on our Global Stock Selection group.

The Global Stock Selection (GSS) Quantitative Research Development (QRD) group enables and supports Global Stock Selection Research at AQR. GSS QRD maintains 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. QRD is responsible for signal generation and model construction as well as many other aspects of portfolio construction that go into the running of our strategies. The QRD team works closely with researchers to create new systematic investment strategies and refine existing ones, applying technology expertise along with a practical understanding of markets to implement research ideas and develop systems that further enable the business.

Your Role

As a software engineer at AQR, you will build or extend our:

Scalable, AWS-based storage and computing infrastructure
Generalized research platform
Data ingestion and validation engine
Model computation and signal generation pipeline
Machine learning / Natural Language Processing pipeline

What You’ll Bring

Experience developing software on Linux.
Experience building cloud-native applications using AWS or similar cloud platforms.
Experience working with distributed systems, scalable applications, and large-scale data processing.
Proficiency with SQL and experience working with structured data at scale.
Hands-on experience building or integrating native AI applications, including practical experience working with large language models (LLMs), AI agents, or AI-assisted workflows.
Solid understanding of the strengths, limitations, and practical tradeoffs of LLMs, agentic systems, and modern AI technologies.
Ability to take open-ended business or technical problems, work with stakeholders to clarify requirements, and develop practical, well-reasoned technical solutions.
Comfortable operating in environments where requirements evolve, exercising sound engineering judgment rather than relying on detailed specifications.
Strong software engineering fundamentals, including system design, testing, code quality, and maintainability.
Excellent communication and collaboration skills, with the ability to explain technical concepts and work effectively across teams.
Experience supporting quantitative research, machine learning platforms, or financial technology applications is a plus.
Familiarity with investment research, signal generation, portfolio construction, or trading systems is a plus.

Who You Are

Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related technical field.
0-5 years of professional software engineering experience building production software.
Strong programming skills in Python; experience with C++ is a plus.

The salary range for this role is expected to be $135,000 to $155,000. This is the range that we in good faith believe is accurate for this role at the time of this posting. We may ultimately pay more or less than the posted range, depending upon factors such as skills, experience, location, or other business and organizational needs. This wage range may also be modified in the future.