C++ Software Developer
Company | DRW |
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Location | Chicago, IL, USA |
Salary | $175000 – $175000 |
Type | Full-Time |
Degrees | Bachelor’s, Master’s, PhD |
Experience Level | Entry Level/New Grad |
Requirements
- A degree in Computer Science, Computer Engineering, or any related engineering discipline graduating between December 2025 and June 2026 (Bachelor’s, Master’s, PhD)
- Excellent software development skills in modern C++
- A strong understanding of object-oriented design, data structures and algorithms
- A solid foundation in programming with the ability to think, communicate, and code clearly
- An eagerness to learn and apply what you have learned, and to know the impact of your code
- Strong problem-solving and analytical reasoning skills
- Strong communication and collaboration skills
Responsibilities
- Learn the inner workings of a low latency automated trading platform
- Learn the fundamentals of trading and how technology enables the workflow
- Design, develop and test proprietary software, including: Low level optimizations for data processing, Automated trading strategies, Risk management platform, Adapters for exchange protocols, Real time trading simulator for strategy testing, Robust inter process communication mechanisms
- Collaborate with experienced teammates to learn and implement custom solutions that balance speed, features, and cost to improve our technology stack
- Work with researchers to implement research studies, simulate trading strategies, and develop algorithms to compete in financial markets
- Design trading strategy simulation software optimized for high fidelity and throughput
- Develop software for large scale data acquisition, storage, access, and visualization
- Create user interfaces for data exploration and post trade analysis
- Accelerate the research lifecycle from strategy prototype to production deployment
Preferred Qualifications
- Experience in high-performance, multi-threaded, network programming
- Previous exposure to trading systems or the trading industry
- Low level understanding of machine learning pipelines