Founding AI Engineer @Plato (backed by Hexa)

Plato is building the AI OS for litigation lawyers.

With AI, the legal industry is on the verge of a major transformation. Litigation lawyers are overwhelmed by complex and repetitive tasks: complex damage calculations, thousands of documents to review, days spent drafting legal documents, and more.

Our mission: Build the leading AI OS for litigation lawyers, enabling professionals to work 5x faster, by putting their whole firm on autopilot.

Plato already supports more than 30 law firms and 1 major insurance company’s legal team, who use our solution daily to manage hundreds of active cases.

Founded in January 2026 by Ben (CEO) and Alexandre (CTO), we are backed by Hexa (formerly eFounders), Europe’s leading startup studio. Hexa has launched over 35 companies, including unicorns such as Front, Aircall, and Spendesk, collectively generating more than €230M in ARR and raising over €750M in funding.

Plato is live with real users, real data and real revenue, solving concrete legal workflows in production. We’re now looking for a Founding AI Engineer to help us turn the latest advances in AI into reliable products used every day by legal professionals.

🎯 Your mission

You’ll work directly with Alexandre , our CTO, at the intersection of AI, software engineering and product.

Your job is not to do AI research for the sake of research. You’ll experiment with models and approaches, understand what works, and turn the best solutions into reliable production systems.

You’ll work on problems such as building agentic workflows, making sense complex legal documents, designing autonomous long-running agents, and improving the quality and reliability of AI-generated outputs.

Responsibilities

Build AI-powered product features

• Design and ship AI features used directly by lawyers

• Be at the bleeing edge of the agentic stack: memory, sandboxes, code mode, lots of tools, subagents…

• Turn prototypes and experiments into production-ready feature

Work with complex data

• Extract, structure and reason over large volumes of unstructured legal documents

• Build robust data and document-processing pipelines

• Experiment with different models and approaches to improve accuracy and reliability

Make AI work in production

• Evaluate and improve agent outcomes through testing, evals and iteration

• Optimize systems for quality, latency and cost

• Build the backend services and infrastructure needed to run AI reliably at scale

Shape the product

• Work directly with the founders and users to understand real-world problems

• Move quickly from problem → experiment → production (think days, not weeks)

• Take ownership of your projects from exploration to deployment