Role overview



Our Multi-Asset Arbitrage and Credit organization is seeking strong engineering talent.



You'll build research, analytics, and risk applications, combining strong software engineering skills with practical AI knowledge.



We welcome backgrounds in engineering, quantitative research, and mathematics.




Key responsibilities

• Design and build data pipelines and AI-enabled platforms that support investment research, analytics, and risk workflows.

• Work with portfolio managers, analysts, and quants to understand workflows, define requirements, and improve applications.

• Build APIs and data integrations using Python or Rust, working with central AI teams.

• Own applications from prototype through production, including testing, deployment, monitoring, performance, access controls, and error handling.

• Integrate LLMs, document retrieval, and structured data extraction, with accuracy checks and source references.




Minimum qualifications

• 3+ years in software development, quantitative development, or quantitative research, including shipping and supporting production software.

• Strong skills in either Python or Rust, plus SQL; grounding in data structures, algorithms, and system design.

• Experience with API design, automated testing, debugging, and CI/CD.

• Practical knowledge of LLM APIs, retrieval, tool calling, structured outputs, and evaluating model accuracy and limitations.

• Ability to break down ambiguous problems, weigh technical tradeoffs, and explain decisions to nontechnical users.

• Master's degree in Computer Science, Mathematics, Statistics, or a related field, or equivalent experience.




Preferred qualifications

• Production AI applications, including RAG or agent workflows using OpenAI, Anthropic, or comparable services.

• Applied statistics or machine learning, including model validation.

• Financial datasets or investment workflows, particularly credit, relative-value, derivatives, or portfolio and risk analytics.

• Cloud platforms, preferably AWS; Docker, Kubernetes, or infrastructure as code.

• Familiarity with FastAPI, React, TypeScript, vector databases, or graph databases.