Senior AI/ML Engineer

Confirmed live in the last 24 hours

Inovalon

Compensation

$151,800 - $228,000/year

Atlanta, GA; Nashville, TN

Hybrid

Posted January 26, 2026

Job Description

Inovalon was founded in 1998 on the belief that technology, and data specifically, would empower the transformation of the entire healthcare ecosystem for the better, improving both outcomes and economics. At Inovalon, we believe that when our customers are successful in their missions, healthcare improves. Therefore, we focus on empowering them with data-driven solutions. And the momentum is building.

Together, as ONE Inovalon, we are a united force delivering solutions that address healthcare’s greatest needs. Through our mission-based culture of inclusion and innovation, our organization brings value not just to our customers, but to the millions of patients and members they serve.

Job Title: Senior AI / ML Engineer

About Us:

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting-edge technology to improve health outcomes and streamline healthcare processes. We are looking for a talented and experienced Senior Full Stack Machine Learning Engineer to join our dynamic team.

Job Description:

As a Senior AI/ML Engineer, you will play a pivotal role in designing, developing, and deploying machine learning models that drive our healthcare solutions. You will work closely with data scientists, software engineers, and product managers to build scalable and robust machine learning systems. Your expertise will help us transform healthcare data into actionable insights, ultimately improving patient care and operational efficiency.

Key Responsibilities:

Model Development: Design, implement, and optimize machine learning models for various healthcare applications, including predictive analytics, natural language processing, and generative AI.

End-to-End Deployment: Develop and maintain the full lifecycle of machine learning solutions, from data preprocessing and model training to deployment and monitoring in production environments.

Data Engineering: Collaborate with data engineers to build and maintain data pipelines, ensuring the availability of high-quality data for training and inference.

Software Development: Write clean, efficient, and maintainable code for machine learning applications, ensuring seamless integration with existing systems.

Performance Optimization: Continuously monitor and improve the performance of machine learning models and systems, addressing issues related to scalability, latency, and accuracy.

Collaboration: Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.

Mentorship: Provide guidance and mentorship to junior engineers, fostering a culture of continuous learning and innovation.

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