Quantitative Developer

Overview

Build the core research and trading platform for DRW's new systematic, mid-frequency trading business, working at the intersection of quantitative research and software engineering to turn research ideas into reliable systems that trade.

What you'll do

• 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 development practices, testing standards, and operational processes
• Take systems and strategies from prototype to production and remain accountable for their reliability once they are live

What you'll need

• 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
• Clear communication and a collaborative working style, particularly when working across research and engineering disciplines

Nice to have

• 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

Details

• Location: New York office
• Work mode: in the NY office 5 days per week
• Experience in trading or finance is not required