Data Infrastructure Engineer at Stealth AI data startup founded by Cambridge ML researchers

You will own the end-to-end data function for a high-output team of five that outperforms departments ten times its size. By building sophisticated infrastructure to turn 100k+ unstructured financial documents into high-fidelity datasets, you will directly enable the world's largest asset managers and investment banks to make million-dollar decisions.

• Architect and maintain robust data pipelines for the ingestion, transformation, and distribution of massive unstructured datasets from PDFs.
• Collaborate with and build new autonomous AI agents (like LISA and DAN) to automate complex document parsing and validation.
• Ensure absolute data integrity and quality for institutional clients, owning the infrastructure that keeps datasets current and correct 24/7.

• 3–5 years of experience building production data pipelines or ML systems using Python, Postgres, and asynchronous task managers.
• A product-first mindset with an obsession for data quality and experience shipping in fast-paced startup environments of 2–30 people.
• Deep familiarity with agentic tooling and LLMs, using Claude Code or Codex daily as a primary part of your development workflow.

• Founding-level impact in a lean team where five people produce the output of fifty through advanced AI agent orchestration.
• Direct exposure to leadership from Cambridge ML research and successful exits, working at the intersection of finance and agentic AI.
• Clear trajectory to a CTO path or a world-class education in building production-grade data systems for the next era of financial services.