AI Engineer

Company Description

Today, when you go to your doctor and get referred to a specialist (e.g., for sleep apnea), your doctor sends out a referral and tells you, "They'll be in touch soon." So you wait. And wait. Sometimes days, weeks, or even months. Why? Because too often specialists and medical services are overwhelmed with referrals and the painstakingly manual process it takes to qualify your referral prevents them from getting around to it on time, or sometimes at all. Tennr prevents these delays and denials by making sure every referral gets where it needs to go, with the right info, at the right time. Powered by RaeLM™ Tennr reads, extracts, and acts on every piece of patient information so providers can capture more referrals, slash denials, and reduce delays.

Role Description

We're looking for a high-agency AI Engineer to bridge the gap between cutting-edge ML research and real-world product delivery . You'll design and build agentic workflows that power complex healthcare processes, combining LLMs, vision models, and structured automation to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare.

This role involves creating pipelines , evaluation harnesses, and scalable production-grade agents . You'll be responsible for researching the right tools and models , ensuring we always use the best-fit technology for each workflow.

As part of our ML team , you'll work hand-in-hand with researchers to translate new techniques and insights into production-ready systems. The ideal candidate has full-stack engineering experience , hands-on expertise with LLM APIs , and a proven track record of deploying AI systems into production . You'll work across the stack-from data collection to orchestration to frontend integration -bringing cutting-edge AI solutions to life in real-world healthcare environments.

Responsibilities

As an AI Engineer at Tennr, you'll bridge the gap between cutting-edge ML research and real-world product delivery. You'll help build intelligent systems that use LLMs, vision models, and structured automation to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare.

• Ship full-stack AI systems end-to-end
• Build observability and debugging tools to capture model performance, user feedback, and long tail cases in production.
• Go from ideation to code within hours; iterate quickly on experiments and data.
• Work directly with ML, Sales, Customer Success, and Implementations to build AI systems for different use cases

Qualifications

Why Tennr?

Benefits