Quantitative Developer

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.

We are building a new systematic, mid-frequency trading business at DRW. The strategy combines diverse data, modern machine learning, and high-performance software systems to identify and trade opportunities at scale.

This is an early-stage effort within an established trading firm: the team has the opportunity to design its technology and research platform from the ground up while benefiting from DRW's capital, data, compute infrastructure, market access, and institutional experience.

We are a small, fast-moving team of quantitative researchers and developers. Engineers are not a support function—they are central to how we conduct research, put strategies into production, and build a lasting competitive advantage.

As a Quantitative Developer / Research Engineer, you will be an early member of the team with meaningful ownership of its systems, research tooling, and engineering practices. You will work closely with experienced researchers and trading-system engineers across the team and the firm, combining substantial autonomy with strong technical mentorship.

You will work at the intersection of quantitative research and software engineering, turning research ideas into reliable systems that trade. The ideal candidate combines strong software engineering fundamentals with an interest in quantitative research, machine learning, and data-intensive systems.

This role will require you being in the NY office 5 days per week.

• Design and build the core research and trading platform, including data pipelines, backtesting and simulation frameworks, portfolio and execution tooling, and research APIs
• Work closely with quantitative researchers to implement studies, test hypotheses, and translate promising ideas into robust production systems
• Develop and productionize statistical and machine learning models, owning the workflow from feature generation and training through backtesting, deployment, and live monitoring
• Build reliable data infrastructure for large historical and real-time datasets, with an emphasis on point-in-time correctness, reproducibility, performance, and ease of use
• Improve the performance and scalability of computationally intensive research and production workloads
• Contribute to foundational architecture and engineering decisions, working with experienced trading-system engineers to establish the development practices, testing standards, and operational processes the team will use as it grows
• Take systems and strategies from prototype to production and remain accountable for their reliability once they are live

• A bachelor's, master's, or PhD degree in computer science, computer engineering, or another technical field
• At least two years of experience developing production software, primarily in Python and/or C++, with the ability and willingness to work across languages when needed
• Strong computer science fundamentals and sound instincts in software design, debugging, testing, and performance analysis
• The ability to enter an unfamiliar system, develop a clear mental model of it, and identify practical ways to improve its reliability, simplicity, and performance
• Fluency in a UNIX/Linux environment and a working understanding of operating systems, concurrency, networking, and system performance
• A track record of scoping and delivering production systems in fast-moving or ambiguous environments
• High ownership, good judgment, and a bias toward action—you identify risks early, reduce unnecessary complexity, and take pride in building systems that others rely on
• Clear communication and a collaborative working style, particularly when working across research and engineering disciplines

• Experience developing deep learning systems with PyTorch
• Experience with GPU computing, kernel development, distributed training, or performance optimization
• Hands-on experience building and operating machine learning or data pipelines in production
• Experience developing large-scale, concurrent, high-throughput, or performance-sensitive systems
• A strong foundation in mathematics, statistics, optimization, or machine learning
• Experience building tools and infrastructure for quantitative researchers, data scientists, or similarly technical users

• Build from an early stage — Help shape a new systematic trading business, with broad scope, short feedback loops, direct influence over how the team operates, and the opportunity to share in its success
• Autonomy with mentorship — Make meaningful technical decisions while learning from experienced researchers and trading-system engineers across the team and the firm
• Broad, end-to-end ownership — Take systems from research and development through deployment and live trading, working across software, data, machine learning, and financial markets
• AI-native engineering — Work in an environment where AI-assisted coding, testing and research are deeply embedded in the development workflow