Software Engineer, Research – Cumberland Systematic

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world.

We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets.

We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds.

The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

Cumberland Systematic is a multi-asset trading team focused on mid and high-frequency fully systematic strategies in the delta one space.

We trade 24/7 globally with our team located primarily in Chicago and London.

We are looking for a Software Engineer to join our team.

You will work with other software engineers to design and develop the full stack needed to enable a global trading operation with high availability requirements.

You will be expected to design and develop integrations with data vendors, blockchain infrastructure, data management solutions, and libraries for managing and analyzing data.

The team’s existing systems are written in Python and C++.

Candidates should have strong initiative and proven experience independently driving projects to completion.

We work from high-level requirements and engineers are expected to gain an intimate understanding of the business and work directly with quantitative researchers on a daily basis.

• Design, develop, test, document, and maintain software and infrastructure needed for quantitative financial research
• Accelerate the research lifecycle from data engineering to feature generation and analysis