Quantitative Researcher - Machine Learning

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.

The Machine Learning Researcher will have a deep understanding of the principles behind modern ML algorithms — including recent advances such as transformer-based architectures and other state-of-the-art frameworks — and the ability to turn that knowledge into high-impact, production-ready solutions.

The role involves applying advanced ML techniques to a wide range of forecasting challenges, building scalable ML pipelines, and deploying them in production, while working with high-dimensional, noisy, structured and unstructured datasets. Experience applying ML models in financial markets is desirable, but exceptional candidates with a strong ML background from tech, startups, or academia will also be considered.

Responsibilities

• Research, design, and deploy robust ML models.

• Build and maintain scalable, production-level ML pipelines.

• Extract signals from large, noisy, real-world datasets.

Qualifications

• PhD (or exceptional MSc) in ML, Computer Science, or related field.

• Deep theoretical and practical knowledge of core ML algorithms, and comfortable experimenting with model architectures, feature engineering, and hyperparameter tuning to produce high-performance and resilient models.

• Proven experience taking ML models from research to live production.

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