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The role involves building and supporting production-level trading systems in C++ for credit and rates products, collaborating with traders and quantitative researchers.

Design, build, and operate cloud-native platform services and distributed systems to support research, AI, and data-intensive workloads. Create abstractions, APIs, and automation for cloud resource management; manage large-scale compute, storage, and networking; improve observability, performance, and resiliency; collaborate with researchers to deliver scalable production systems.

Build full-stack, low-latency trading and pricing tools for options traders. Collaborate with traders, researchers, and business stakeholders to design intuitive frontends and scalable backend services, prototype and productionize distributed systems, and expand trading capabilities globally while improving performance, stability, and usability.

Design, implement, and maintain low-latency, high-performance network infrastructure for trading systems. Partner with engineering teams to automate, monitor, and troubleshoot distributed networks, optimize performance, ensure resilience and security, and evaluate emerging technologies to improve scalability and reliability.

Build and optimize low-latency, high-performance C++ trading systems for crypto markets; partner with researchers and traders to productionize strategies; optimize latency, throughput, reliability; implement pricing, risk, and execution logic; analyze large-scale market and trade data; ensure resilience, testing, and scalability across global venues.

As a Machine Learning Engineer, you will enhance internal libraries for deep learning, collaborate with researchers, and optimize workflows for cost savings.

Analyze complex datasets, build predictive models, and communicate findings to stakeholders while collaborating with data engineers and quantitative researchers.

Design, train, and deploy deep-learning models that predict market prices; build tooling and large-scale ML systems to enable firm-wide ML adoption; simulate trading decisions and integrate model predictions into production trading systems.

Build high-performance trading tools and large-data research platforms in small teams. Design system architectures, develop resilient production software, and apply distributed computing, NLP, and machine learning to solve complex quantitative problems while collaborating with engineers, traders, and researchers.

Summer internship conducting quantitative research to develop, backtest, and implement predictive trading models using advanced statistical and machine learning techniques, unconventional data sources, and coding in Python/R/C++. Collaborate with senior researchers and participate in live trading deployment and analysis.