Job Description
Join Quantum Derivatives Tech as a Part-Time Equity Options Software Engineer and revolutionize financial trading technology. We're seeking a passionate expert to build cutting-edge solutions for our institutional clients. This hybrid role offers the flexibility of remote work with occasional on-site collaboration in our Manhattan office. You'll work on high-performance trading platforms that process millions of transactions daily, directly impacting global financial markets.
Our ideal candidate thrives in fast-paced environments, values precision, and possesses deep knowledge of equity derivatives. You'll collaborate with world-class quants, traders, and engineers to develop scalable systems that optimize option pricing models and risk analytics. Enjoy competitive compensation, flexible scheduling, and opportunities to shape the future of fintech.
Responsibilities
- Design and implement low-latency trading systems for equity options derivatives
- Develop and optimize option pricing algorithms using Monte Carlo and binomial models
- Build risk management frameworks for complex options portfolios
- Create real-time data pipelines for market feeds and volatility surfaces
- Automate regulatory compliance reporting (SEC, FINRA)
- Collaborate with traders to translate business requirements into technical specifications
- Maintain and enhance existing C++/Python-based trading infrastructure
- Conduct performance testing and optimization for high-frequency trading systems
Qualifications
- Bachelor's degree in Computer Science, Mathematics, or Finance (Master's preferred)
- 3+ years of experience in equity options software development
- Expertise in C++ and Python with strong OOP fundamentals
- Deep understanding of Black-Scholes, stochastic calculus, and option Greeks
- Experience with FIX protocol and market data systems (Bloomberg, Refinitiv)
- Familiarity with Linux, TCP/IP networking, and low-latency architectures
- Proficiency in version control (Git) and CI/CD pipelines
- Ability to work independently with minimal supervision in part-time capacity