Requirements

8+ years’ experience with a proven track record in building and operating large scale distributed systems
Strong software engineering skills in Python, Java, Scala, or C++
Deep understanding of cloud native architectures and modern infrastructure platforms
Experience building data intensive applications, streaming systems, or machine learning platforms
Strong collaboration and communication skills with the ability to work closely with both engineering and business stakeholders
Intellectually curious about markets, data and complex systems, with the motivation to solve difficult technical problems at the intersection of engineering and quantitative research
Excited by rapid experimentation, continuous learning and building platforms that directly influence investment research, signal discovery and trading decision making

Nice to Haves

Quantitative trading, market structure, electronic trading, or fintech experience
Machine learning, generative AI, or agentic AI platform development
High performance computing, real-time analytics, or large scale data platforms
Experience with modern research data platforms and high-performance analytics technologies
Advanced degree in Computer Science, Engineering, Mathematics, or a related quantitative discipline

Real-time streaming data platforms using Kafka, Flink, Spark, and cloud-native technologies
Distributed computing systems for large-scale Quantitative research enablement and simulation
AI and machine learning infrastructure, including training, inference, feature management, and model lifecycle tooling
Agentic and generative AI applications for quantitative workflows
Cloud-native Software Engineering leveraging AWS and modern data architectures

Join a team building strategic capabilities at the center of Goldman Sachs' quantitative and electronic trading businesses
Combine the scale and complexity of a leading technology company with the impact, pace, and intellectual rigor of a premier financial institution