Today, when you go to your doctor and get referred to a specialist (e.g., for sleep apnea), 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 specialists and medical services are overwhelmed with referrals and the painstakingly manual process it takes to qualify your referral prevents them from getting around to it on time, or sometimes at all. Tennr prevents these delays and denials by making sure every referral gets where it needs to go, with the right info, at the right time. Powered by RaeLM™ Tennr reads, extracts, and acts on every piece of patient information so providers can capture more referrals, slash denials, and reduce delays.
ResponsibilitiesMachine 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 Model Development: Architect, train, deploy, and monitor machine learning models, especially open-source LLMs and traditional computer vision models, to deliver tangible value to Tennr’s customers.
Data Processing & ML Ops: Build, optimize, and maintain data pipelines and machine learning infrastructure, enhancing our operational capability as data volume and complexity grow.
System Integration: Design and implement backend workflows leveraging machine learning to automate critical processes within Tennr’s platform.
Custom Model Development: Fine-tune large language models, vision language models, and traditional computer vision models tailored specifically for medical document understanding and related tasks.
Product Collaboration: Work closely with sales, customer success, and implementation teams to integrate customer feedback into actionable improvements and innovative solutions.
Evaluation and Optimization: Develop robust evaluation frameworks to measure model performance and efficacy within complex, multi-model systems.
3+ years of professional experience in an applied machine learning engineering role, ideally post-BS/MS.
Proven experience deploying and maintaining machine learning models in production environments, preferably within startups or rapidly evolving companies.
Practical experience fine-tuning open-source LLMs and/or traditional computer vision models.
Familiarity with robust ML Ops stacks, infrastructure management, and operational best practices for machine learning at scale.
Comfort working within complex systems that integrate multiple machine learning components, with experience building evaluation frameworks to ensure system performance.
Academic background in machine learning, mathematics, or related fields preferred.
Up-to-date knowledge of recent advancements in large language models (LLMs) and enthusiasm for tracking rapid developments in the space.
Prior startup experience or exposure to environments leveraging state-of-the-art ML models strongly preferred.
Drive Impact: one of our company values is Cowboy, meaning you set the pace. You won’t just talk about things, you’ll get them done. And feel the impact.
Develop Operational Expertise: learn the inner workings of scaling systems, tools, and infrastructure
Innovate with Purpose: we’re not just doing this for fun (although we do have a lot of fun). At Tennr, you’ll join a high-caliber team maniacally focused on reducing patient delays across the U.S. healthcare system.
Build Relationships: collaborate and connect with like-minded, driven individuals in our Chelsea office 4 days/week (preferred)
Free lunch! Plus a pantry full of snacks.
New, spacious Chelsea office
Unlimited PTO
100% paid employee health benefit options
Employer-funded 401(k) match
Competitive parental leave
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What We Do
Tennr reads documents from incoming faxes passing through healthcare practices and automates essential tasks like scheduling and qualifying patient visits. By automating that paperwork with Tennr, practices receive more patient referrals and reduce billing errors by 98%, significantly growing revenue.
