Senior Backend Engineer (ML)

Tennr•New York, NY

6d•$190,000 - $215,000•Hybrid

About The Position

We’re looking for a high-agency Senior Backend 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 to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare. This role involves creating data pipelines, evaluation harnesses, and scalable production-grade agentic systems. 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 training pipelines to AI agent orchestration to frontend integration - bringing cutting-edge AI solutions to life in real-world healthcare environments.

Requirements

Proficiency in full-stack development, with a strong understanding of system design, backend engineering, and observability infrastructure.
A track record of working through the full lifecycle of building, testing, deploying, scaling, and monitoring LLM-centered software architectures.
Excellent communication and collaboration skills: aligning on interfaces, navigating tradeoffs, and driving cross-team execution.

Nice To Haves

Full-stack engineering experience
Hands-on expertise with LLM APIs
Proven track record of deploying AI systems into production

Responsibilities

Bridge the gap between cutting-edge ML research and real-world product delivery.
Help build agentic systems that use LLMs, and 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.
Design resilient systems for model deployment, evaluation, and monitoring that stay reliable as traffic grows.
Go from ideation to code within hours; iterate quickly on experiments and data.
Collaborate with ML engineers, backend engineers, and cross-functional teams to integrate models cleanly with data pipelines and product.