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.

Responsibilities:

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

Integrate with diverse data sources such as blockchains, exchanges, and data vendors

Test at the unit, functional, and integration levels

Work within in a small team as well as independently with a strong user focus

Provide on-call support as needed

To qualify for this role, you must have:

Python experience gained through professional experience

Built complex systems in modern Python that are maintainable and safely refactorable

Hands-on experience managing large, evolving timeseries data

A working knowledge of type safety and type systems

Strong verbal and written communication skills

Strong internal motivation and a continual desire to learn

Bonus points if you have:

Contributions to open-source crypto projects

Production experience running full or archive blockchain nodes at scale

Collaborated with quantitative researchers

A working knowledge of high-level machine learning concepts and lifecycle

Experience with functional programming and immutable design principles

Practical knowledge of major blockchain types and clients

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