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OpenAI

Backend Software Engineer - Codex for Finance

San Francisco (Remote)$293k–$325kfulltimemidAdded 2 days ago

About this role

OpenAI is seeking a backend engineer to build scalable systems powering AI-driven financial workflows. You'll design data infrastructure, enterprise integrations, and agentic systems that make advanced AI reliable and trustworthy for financial institutions, with significant ownership over zero-to-one product development.

What you'll do

  • Design and build backend systems for AI-native financial workflows and data pipelines
  • Develop infrastructure to ingest, index, and serve financial data and market information at scale
  • Create integrations with financial data providers and enterprise systems with secure authentication and authorization
  • Build systems ensuring models produce accurate, verifiable outputs with clear source provenance
  • Improve production system reliability, latency, observability, and develop evaluations for financial workflows
  • Partner with financial institutions to understand needs, unblock deployments, and drive product improvements

What they're looking for

  • Backend development (Python, Go, Rust, or TypeScript)
  • API design and distributed systems
  • Data-intensive applications and retrieval systems
  • Enterprise security, authentication, and authorization
  • System reliability and observability
  • Product thinking and cross-functional collaboration
  • Agile integration of AI/LLM capabilities
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OpenAI

OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.

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Likely interview questions

  • Describe a complex backend system you built from scratch—what were the key architectural decisions and trade-offs?
  • How have you approached building data ingestion and retrieval systems at scale, and what challenges did you encounter?
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