platform engineer for quantitative research

Goldman Sachs is a global investment banking, securities, and investment management firm. Its quantitative enablement platform connects extensive market, transactional, and alternative data with quantitative talent to accelerate the path from research ideas to live trading strategies.

• Build a platform, infrastructure, AI-assisted workflows, and scalable compute to accelerate quantitative research and trading capabilities
• Partner with quantitative researchers, portfolio managers, traders, and engineering teams to define and deliver research capabilities
• Build high-throughput distributed systems that process large-scale market, transactional, and alternative data with low latency, resiliency, and operational excellence
• Develop research workflows incorporating large language models, agentic systems, model context protocols, and machine learning infrastructure
• Build AI-native research capabilities combining language models, agentic systems, knowledge graphs, vector search, quantitative datasets, and experimentation frameworks
• 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
• Work on real-time streaming data platforms using Kafka, Flink, Spark, and cloud-native technologies
• Build distributed computing systems for quantitative research enablement and simulation
• Develop AI and machine learning infrastructure for training, inference, feature management, and model lifecycle tooling
• Build agentic and generative AI applications for quantitative workflows
• Leverage AWS and modern data architectures in cloud-native software engineering
• Build low-latency systems supporting research, analytics, and trading use cases
• Develop research platform capabilities, including backtesting frameworks, experiment tracking, feature stores, metadata discovery, distributed research compute, Lakehouse architectures, ClickHouse, vector search, knowledge graphs, and large-scale timeseries platforms

• 8+ Years of experience 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, including the ability to work with engineering and business stakeholders
• Curiosity about markets, data, and complex systems, and motivation to solve technical problems at the intersection of engineering and quantitative research
• Enthusiasm for rapid experimentation, continuous learning, and building platforms that influence investment research, signal discovery, and trading decisions
• Будет плюсом: 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 such as ClickHouse, Databricks, Lakehouse architectures, distributed compute frameworks, and large-scale time series data stores; an advanced degree in Computer Science, Engineering, Mathematics, or a related quantitative discipline

• Локация: Варшава
• Training and development opportunities, firmwide networks, benefits, wellness and personal finance offerings, and mindfulness programs
• Reasonable accommodations are available for candidates with special needs or disabilities during the recruiting process