Quantitative Software Engineer (Python/C++) – Research

P2P; New York (NY); On-site; USD 175,000 - 225,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401k plan

Disability insurance

Life/AD&D insurance

Health Savings Account

Job summary

DRW is seeking a Software Engineer to join the Cumberland Systematic team. You will design and develop the full stack to enable a global trading operation with high availability.

The role involves integrating data sources, blockchains, and data management libraries, with a focus on Python and C++ in a research-driven environment. Responsibilities include building analytics and infrastructure for quantitative research, testing across levels, and collaborating with researchers daily.

Qualifications

• Proficient Python development in production environments.
• Hands-on experience managing large evolving timeseries data.
• Knowledge of type safety and modern Python typing.
• Strong verbal and written communication skills.
• Self-motivated to drive projects to completion.

Responsibilities

• Design, develop, test, document, and maintain software and infrastructure for quantitative research.
• Accelerate the research lifecycle from data engineering to feature generation and analysis.
• Integrate with diverse data sources such as blockchains, exchanges, and data vendors.
• Test at unit, functional, and integration levels.
• Work in a small team or independently with a strong user focus.
• Provide on-call support as needed.

Skills

Python

Timeseries data

Type safety

Communication

Self-motivated learning

Education

Bachelor's degree in CS/Math/Engineering

Job description

DRW is seeking a Software Engineer to join the Cumberland Systematic team. You will design and develop the full stack to enable a global trading operation with high availability.

The role involves integrating data sources, blockchains, and data management libraries, with a focus on Python and C++ in a research-driven environment. Responsibilities include building analytics and infrastructure for quantitative research, testing across levels, and collaborating with researchers daily.