AI Engineer

4 Months ago • All levels • Research Development

Job Summary

Job Description

Tennr is seeking an AI Engineer to bridge the gap between ML research and product delivery. You will build intelligent systems using LLMs, vision models, and structured automation to solve complex challenges in U.S. healthcare workflows. Responsibilities include shipping full-stack AI systems end-to-end, developing observability and debugging tools for model performance and user feedback, iterating quickly on experiments and data, and collaborating with ML, Sales, Customer Success, and Implementations teams to build AI systems for various use cases.
Must have:
  • Proficiency in full-stack development
  • Experience with web frameworks and backend systems
  • Knowledge of cloud infrastructure
  • Proven full lifecycle experience with LLM architectures
  • Understanding AI system performance testing
  • Strong communication skills for articulating ideas
Good to have:
  • Experience in healthcare
  • Experience with unstructured documents

Job Details

About Tennr:

Today, when you go to your doctor and need to be referred to a specialist (e.g., for sleep apnea), your doctor sends a fax (yes, in 2024, 90% of provider-provider communication is a 1980s fax). These are often converted into 20+ page PDFs, with handwritten (doctor’s handwriting!) notes, in thousands of different formats. The problem is so complex that a person has to read it, type it up, and manually enter your information. Tennr built RaeLM™ (7B—trained on 3M+ documents) to read these docs, talk to your doc to ensure nothing is missed, and text you to help schedule your appointment so you can get better, faster.

Tennr is a NYC-based tech company that launched out of Y-Combinator and is backed by Lightspeed Venture Partners, Andreessen Horowitz, Foundation Capital, The New Normal Fund, and other top investors.

Key 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

  • Proficiency in full-stack development, with a strong understanding of web frameworks, backend systems, and cloud infrastructure

  • A track record of working through the full lifecycle of building, testing, deploying, scaling, and monitoring LLM-centered software architectures.

  • Understanding of how to test AI system performance beyond raw model accuracy.

  • Must articulate ideas well! A big part of making successful AI systems is telling people about them. This includes writing docs and technical reports at the minimum – and speaking in front of an audience at the extreme (optional).

  • Experience in healthcare or working with unstructured documents is a plus. 

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New York, New York, United States (On-Site)

New York, New York, United States (On-Site)

New York, New York, United States (On-Site)

New York, New York, United States (On-Site)

New York, New York, United States (On-Site)

New York, New York, United States (On-Site)

New York, New York, United States (On-Site)

New York, New York, United States (On-Site)

New York, New York, United States (On-Site)

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