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

Goldman Sachs Electronic Market Making – Software Engineer – Vice President – New York

The Electronic Market Making (EMM) group is embarking on a major, multi-phase ETF initiative. As part of this effort, we are seeking a senior-level engineer to architect and build the foundational trading infrastructure that will power this new business line. This is a high-impact, self-directed role with no distinction between research and production environments.

We are a small team that demands a high level of ownership and accountability from each contributor, while strongly emphasizing collaboration and transparency. Engineers on the team are deeply involved in both software and strategy development. We are seeking a dedicated and ambitious engineer who thrives in a challenging environment and wants to make a meaningful impact at the firm.

Basic Qualifications

• 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)