About the job
Inovalon, established in 1998, operates on the belief that technology and data can significantly enhance the healthcare ecosystem, leading to better outcomes and financial efficiencies. Our commitment to our clients' success directly translates to improved healthcare for all. We are dedicated to providing data-driven solutions that empower our customers to excel in their missions.
As a unified organization, Inovalon strives to deliver impactful solutions that meet the most pressing needs of the healthcare sector. Our culture promotes inclusion and innovation, ultimately benefiting not just our clients, but the millions of patients and members they serve.
Job Title: Senior Full Stack Machine Learning Engineer
About Us:
Inovalon is at the forefront of healthcare technology, aiming to transform the industry through advanced AI and machine learning solutions. We are on a mission to harness state-of-the-art technology to enhance health outcomes and optimize healthcare processes. We invite you to join our vibrant team as a Senior Full Stack Machine Learning Engineer.
Job Description:
- Your role as a Senior Full Stack Machine Learning Engineer will be crucial in designing, developing, and deploying machine learning models that enhance our healthcare solutions. Collaborating with data scientists, software engineers, and product managers, you will build scalable and robust machine learning systems that transform healthcare data into actionable insights, significantly improving patient care and operational efficiency.
Key Responsibilities:
- Model Development: Create, implement, and refine machine learning models for diverse healthcare applications, focusing on predictive analytics, natural language processing, and generative AI.
- End-to-End Deployment: Oversee the complete lifecycle of machine learning solutions, from data preprocessing and model training to deployment and monitoring in production settings.
- Data Engineering: Collaborate with data engineers to construct and maintain data pipelines, ensuring the provision 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 assess and enhance the performance of machine learning models and systems, addressing scalability, latency, and accuracy issues.
