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Your Impact
The future of quantitative investing belongs to platforms that turn massive data into ideas, signals, and decisions in real time.
Goldman Sachs has the raw materials few firms can match: one of the deepest sets of market, transactional, and alternative data in finance, and one of the largest concentrations of quantitative talent anywhere; traders, researchers, strategists, and systematic PMs across global markets.
What we are building is the platform that connects the two - compressing the path from research idea to live trading strategy from weeks to hours, for every researcher at the firm, on a single platform.
As an engineer, your work will directly influence how quantitative researchers, and traders discover signals, test investment ideas, build capabilities that improve and translate research velocity, support alpha generation and strengthen into production trading decisions.
What You'll Do
Build the Platform for Quantitative Research
Partner directly with quantitative researchers, portfolio managers, traders, and engineering teams to define and deliver high impact research capabilities, building the platform, infrastructure, AI assisted workflows, and scalable compute that accelerate signal discovery, experimentation, back testing, deployment, and production monitoring, reducing the time from idea to production from weeks to hours.
Engineer at Scale
Build high throughput distributed systems capable of processing petabytes of market, transactional, and alternative data while maintaining low latency, resiliency, and operational excellence.
Enable AI First Research
Develop next generation research workflows that incorporate large language models, agentic systems, model context protocols (MCP), and modern machine learning infrastructure to accelerate discovery and insight generation.
Build AI native research capabilities that combine large language models, agentic systems, knowledge graphs, vector search, quantitative datasets and experimentation frameworks to make research workflows faster, more discoverable and more scalable.
Raise the Engineering Bar
Drive architecture, reliability, observability, automated testing, and software development best practices across a globally distributed platform.
Own critical platform capabilities end to end, from concept and architecture through implementation, adoption and production operations, with the opportunity to shape long term platform strategy and engineering standards.
What You'll Work On
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
Low latency systems supporting research, analytics, and trading use cases
Research platform capabilities including back testing frameworks, experiment tracking, feature stores, meta data discovery, distributed research compute, Lakehouse architectures, ClickHouse, vector search, knowledge graphs and large scale timeseries platforms
What We're Looking For
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
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