Machine Learning Engineer, Tennr
Automation platform for healthcare operations
Be an early applicant
Automation platform for healthcare operations
21-100 employees
Be an early applicant
21-100 employees
Company mission
To ease the administrative burdens on healthcare providers.
Role
Who you are
- 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
- 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
What the job involves
- 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
Our take
In the healthcare system, there are inefficiencies associated with the referral process to specialists. Often coming in various forms, from email to fax, without any standardized structure, this leads to administrative burdens and significant delays in patient care.
Tennr has developed a machine learning and document scanning platform that automates the extraction of relevant information from referrals and facilitates faster appointment scheduling. With its focus solely on the healthcare industry, it addresses the complexities of medical terminology and ensures doctors, clinics and insurers stay informed throughout the process.
It's off to a successful start, with early adopters reporting significant improvements in their workflows, signalling strong market demand for the solution. This success is accompanied by early funding, enabling the company to focus on accelerating its growth for a bright future in the healthcare automation sector.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Oct 2024
$37m
SERIES B
Mar 2024
$18m
SERIES A
Total funding: $55.2m
Company HQ
Chelsea, New York, NY
Articles
Leadership
Trey Holterman
(Co-Founder & CEO)They studied Computer Science with a focus on Machine Learning at Stanford. Prior to starting Tennr, they focused on applying their research in the health technology world at HealthIQ and Strava.
Diego Baugh
(Co-Founder & CTO)They studied Computer Science at Stanford with a focus on Human Computer Interaction and Design. Prior to Tennr, they built automation systems at Goldman Sachs as an Automation Engineer.
Tyler Johnson
(Co-Founder & CTO)They studied Computer Science with a focus on Systems and Networking at Stanford. Prior to Tennr, they worked as a Lead Engineer at Tanduo, where they built bespoke data workflows for large industrial companies.
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