Quantitative Developer

DRW•New York, NY

1d•$175,000 - $250,000•Onsite

About The Position

DRW is a diversified trading firm with over 3 decades of experience. We are building a new systematic, mid-frequency trading business that combines diverse data, modern machine learning, and high-performance software systems. This is an early-stage effort within an established trading firm, offering the opportunity to design the technology and research platform from the ground up while benefiting from DRW's resources. The team is small, fast-moving, and consists of quantitative researchers and developers where engineers are central to research, productionizing strategies, and building 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, combining substantial autonomy with strong technical mentorship. This role requires working at the intersection of quantitative research and software engineering, turning research ideas into reliable trading systems. The ideal candidate has strong software engineering fundamentals with an interest in quantitative research, machine learning, and data-intensive systems. This role requires being in the NY office 5 days per week.

Requirements

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

Nice To Haves

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

Responsibilities

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