About the role
Company Description
Today, when you go to your doctor and get referred to a specialist, 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 providers are overwhelmed with the painstakingly tedious work required to get paid by insurance companies. Powered by proprietary models, Tennr handles the complex paperwork that gets patients through the door and providers paid, helping operators get patients the right care, at the right time, in the right setting.
Role Description
As the first ML Ops Engineer at Tennr, you’ll play a crucial role in building and iterating on foundational Machine Learning and AI systems. You’ll own building machine learning training and inference pipelines that can handle increasing traffic demands and proliferation of product surface as we grow. You will be critical in ensuring our AI-driven healthcare platform is powered by robust, scalable, and efficiently deployed models.
Our Machine Learning team owns and develops multiple in-house, proprietary VLMs, LLMs, and other models that are purpose built for the ambitious problems we are solving in the healthcare space. This is not a role where you are repackaging and wrapping old innovations, but an opportunity to be on the cutting edge of experimentation and productization of net new capabilities. You’ll make impactful contributions and influence fundamental elements of our ML and data systems, expanding Tennr’s ability to rapidly iterate and solve critical problems for patients and providers.
Responsibilities
- Architect, design, and implement ML software systems for deploying and managing models at scale.
- Develop and maintain infrastructure that supports efficient ML operations, including data pipelines, model evaluations, deployments, and training at scale.
- Collaborate closely with ML engineers, software engineers, and cross-functional teams to ensure seamless integration of models with data pipelines and products.
