AI Infrastructure / Product Engineer

Help promising AI experiments become dependable products.

Maximise your chances of a successful application to this job by ensuring your CV and skills are a good match.

Irys is hiring an AI Infrastructure / Product Engineer to build the evaluations, services, and product features that bring applied AI into legal work.

Full-time · Remote · United States only

Annual base salary: $180,000–$260,000 USD. Final base salary within this range depends on relevant skills, experience, and role scope.

What you will build

• Repeatable benchmarks for models, agents, and legal workflows.

• Evaluation pipelines covering accuracy, hallucinations, retrieval quality, latency, and cost.

• Dashboards that make changes in system performance visible.

• Production features that turn research prototypes into usable retrieval, drafting, document, and agent workflows.

• Backend services, APIs, and internal tools that help the team test and release improvements.

• Cloud deployments with monitoring, databases, delivery pipelines, and reliable operations.

What you bring

• Strong Python skills and experience deploying software to the cloud.

• Confidence working with APIs, databases, backend services, and deployment pipelines.

• An interest in LLMs, retrieval-augmented generation, agents, and evaluation.

• The ability to turn an ambiguous idea into a usable, maintainable system.

Full-stack experience is preferred. Experience with FastAPI, Docker, React/Next.js, Postgres, vector databases, AI benchmarks, or document automation would be helpful.

Why Irys

You will work close to AI research while staying connected to the product and its users. The role offers substantial ownership on a distributed team, with direct access to leadership and the opportunity to influence how we measure and improve quality.

Irys was founded by lawyers and AI researchers. We build tools that help legal professionals complete work they can inspect, refine, and stand behind.

Apply

Apply through this listing or at Tell us about an AI system or experiment you built, how you evaluated it, and what you learned when it failed. Share only non-confidential examples. xhyhwjd

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