DRW
Software Engineer, Research – Cumberland Systematic
About this role
DRW's Cumberland Systematic team seeks a Software Engineer to build full-stack infrastructure supporting global systematic trading operations. You'll develop integrations with data vendors and blockchain systems while working closely with quantitative researchers in a Python/C++ environment.
What you'll do
- Design and develop software infrastructure for quantitative financial research and high-availability trading systems
- Accelerate research workflows from data engineering through feature generation and analysis
- Integrate with diverse data sources including blockchains, exchanges, and external data vendors
- Conduct testing across unit, functional, and integration levels
- Collaborate with small team and quantitative researchers with focus on user needs
- Provide on-call operational support as needed
What they're looking for
- Professional Python development with maintainable, refactorable code
- Large-scale timeseries data management
- Type systems and type safety
- Software design and architecture
- Strong communication (verbal and written)
- Blockchain and cryptocurrency systems (bonus)
- Functional programming and immutable design patterns (bonus)
- Machine learning concepts and lifecycle (bonus)
Benefits
- Annual salary $175,000–$225,000 plus discretionary bonus
- Medical, pharmacy, dental, and vision insurance
- 401(k) with discretionary employer match
- Short and long-term disability insurance
- Life and AD&D insurance
- Health savings accounts and flexible spending accounts
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DRW
DRW is a diversified trading firm that builds and maintains trading technology infrastructure, risk management systems, and systematic trading platforms supporting global 24/7 operations across multiple asset classes. The company is hiring Desktop Systems Engineers, Trade Systems Engineers, and Software Engineers to support endpoint infrastructure, trading system reliability, risk analytics, and full-stack trading platform development.
- Website
- drw.com
Likely interview questions
- Tell us about a complex Python system you've built in production. How did you ensure it was maintainable and safely refactorable as it evolved?
- Describe your experience managing large timeseries datasets. What challenges did you face and how did you optimize for performance and correctness?