Machine Learning Engineer

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 RaeLLM™ (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

Machine Learning Engineers at Tennr are expected to wear a variety of hats. In the role, you will be expected to do the following:

• End-to-end product development: architect, train, deploy, and monitor models that drive direct customer value across our product.
• Data processing and ML Ops: optimize scalable data processing pipelines in our platform and maintain machine learning infrastructure.
• Backend integration: design and maintain complex workflows that leverage machine learning to drive automation.
• Product evaluation: Collaborate with sales and customer success teams to respond to feedback from our customers and prospects.
• Custom models: Fine-tune LLMs and VLMs for medical document understanding tasks

Qualifications

• 3+ years of experience (post BS/MS) in an ML research/engineering role
• Proven track record of building and maintaining scalable web applications, particularly in high-volume workflow automation and data processing.
• Experience integrating machine learning models into production environments
• Can efficiently translate open-ended problems into actionable solutions
• Familiarity implementing novel NLP research ideas and techniques. Prior publications in top conference journals is a plus.
• Prior experience in a startup environment is a plus.

Evaluation Criteria

• Technical Proficiency: Your skill in full-stack development and contribution to our tech stack.
• Project Execution: Efficiency and quality in delivering projects within set timelines.
• Innovation and Contribution: Your ability to enhance our system with new ideas and improvements.
• Collaboration and Communication: Effectiveness in team collaboration and clarity in communication.
• Adaptability and Learning: Willingness to embrace new technologies and a commitment to continuous learning.