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# GBM - Public, Electronic Cash Market Making, Quantitative Engineering, Vice President - New York

- **Company:** Goldman Sachs, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $155,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Algorithmic Trading, Systems Engineering, Big Data, C++ (Programming Language), Cloud Engineering, Data Infrastructure, Memory Management, Python (Programming Language), NumPy, Performance Tuning, Software Engineering, Concurrency, Scikit Learn, Information Technology
- **Published:** October 28, 2026
- **Apply:** https://www.dice.com/job-detail/825b5585-3d3f-459b-8b32-7ef6b5728a46

## About the Role

* Bachelor's or Master's degree in a STEM discipline (Computer Science, Physics, Mathematics, or Engineering preferred) with 5 to 10 years of experience developing software architecture in C/C++
 * Expert-level proficiency in C++ is mandatory - candidates whose experience is primarily in Java, managed languages, or cloud-native tooling will not be considered
 * Proven track record of architecting and building high-performance trading systems and data infrastructure from scratch, including custom, bespoke solutions rather than reliance on off-the-shelf libraries or pre-packaged cloud tools
 * Strong low-level system design capabilities, including memory management, concurrency, and performance optimization
 * Demonstrates consistent quality in a fast-paced environment with quick turnaround from testing to production implementation
 * Highly conscientious in maintaining operational robustness while evolving and upgrading current architecture to support future phases (e.g., international ETF expansion)
 * Thrives in a collaborative setting where ideas are openly discussed and challenged
 * Candidates are expected to take an active interest in shaping strategy decisions beyond technical implementation

Preferred Qualifications

 * Background in Telecom, Physics research, or Mathematics research environments where low-level, high-performance system engineering is the norm
 * Experience building and maintaining high-performance research infrastructure for very large data inputs
 * Experience optimizing high-volume, low-latency trading components
 * Familiarity with statistics and data-science methodologies
 * Familiarity with Python and data-science packages (e.g., numpy, scikit-learn)