Quantitative Developer

PVH (Tommy Hilfiger/Calvin Klein); New York (NY); On-site; USD 140,000 - 200,000; Full time

Job summary

DRW seeks a Quantitative Developer / Research Engineer in New York, joining an early team with meaningful ownership of its systems and research tooling. You will work closely with researchers and trading-system engineers, combining autonomy with strong technical mentorship.

This role requires you to be in the NY office 5 days per week, contributing to data pipelines, backtesting, ML modeling, and production-ready infrastructure for large historical and real-time datasets.

Qualifications

• Bachelor’s, master’s or PhD in computer science, computer engineering or a technical field.
• At least two years of production software development in Python and/or C++, with cross-language flexibility.
• Strong CS fundamentals and software design, debugging, testing and performance analysis.
• Ability to enter an unfamiliar system and improve reliability, simplicity and performance.
• Fluency in UNIX/Linux and understanding of OS concepts, concurrency and networking.
• Track record delivering production systems in fast-moving or ambiguous environments.
• High ownership, good judgment, and bias toward action with clear communication and collaboration.

Responsibilities

• Design and build the core research and trading platform, including data pipelines and backtesting frameworks.
• Work with quantitative researchers to implement studies, test hypotheses, and productionize ideas.
• Develop and productionize statistical and ML models from feature generation to live monitoring.
• Build reliable data infrastructure for large historical and real-time datasets with reproducibility.
• Improve performance and scalability of demanding research and production workloads.
• Contribute to architecture decisions and establish development practices and testing standards.
• Take systems and strategies from prototype to production and ensure reliability in live environments.
• Operate in a still-early team with broad scope and meaningful ownership from early stage.

Skills

Python

C++

UNIX/Linux

Software design

Debugging

Performance analysis

Concurrency

Networking

Education

Bachelor's/Master's/PhD in CS/CE

Job description

Company Overview

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.

About the Role

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.

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

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.
• Because the team is still early, the scope is broad and the feedback loop is short. You will have the opportunity to take on meaningful responsibility early while learning from people with deep experience in quantitative research, trading‑system architecture and production trading.

Qualifications

• 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 in trading or finance is not required.