Data Infrastructure Engineer at Arctal — AI Agent Data Platform

Join a high-output team of five reinventing financial data. You will own the entire data infrastructure function, building systems that transform 100,000+ unstructured documents into high-stakes institutional datasets. By leveraging autonomous AI agents, you'll create a scalable engine that outproduces departments ten times its size for the world's leading asset managers.

• Own the end-to-end data lifecycle including ingestion, cleanup, storage, transformation, and distribution of millions of unstructured financial PDFs.
• Architect and maintain high-reliability pipelines and quality monitoring systems that institutional buyers rely on for multi-million dollar decision-making.
• Build and refine the infrastructure for existing and new AI agents, pushing the boundaries of how agentic tooling like Claude Code and Codex are used in production.

• 3–5 years of experience building production data pipelines or ML systems with a "data-as-a-product" mindset and a deep obsession with data integrity.
• Expert-level Python skills with proficiency in Postgres, async programming, task queues, and scraping, alongside daily use of agentic coding tools.
• Proven track record in small, high-autonomy environments (2-30 people) where you drive projects from concept to shipment without needing hand-holding or tickets.

• Massive leverage: Join a lean team of five that produces the output of fifty, using a proprietary fleet of autonomous AI agents (LISA, TIM, DAN, ADA) to disrupt a $100bn+ market.
• Founders with pedigree: Work directly with founders who have backgrounds in Cambridge ML research and have successfully co-founded deep-tech ventures like Secondmind and Sylvera.
• CTO Trajectory: This is a foundational role offering a "fastest-in-market" education in agentic workflows, with a clear path to leadership as the company scales to meet existing institutional demand.