Inovalon
Senior Machine Learning Engineer
Atlanta, GA; Nashville, TN; Remote- United States · Senior
No sponsorship$152k-$202kDetected 27 days ago
PythonFull-Stack DevelopmentGitSQLNoSQLAWSDockerKubernetesMachine LearningDeep LearningPyTorchscikit-learnData AnalysisData ScienceLLMsComplianceAgileEmployment LawHIPAAProblem Solving
About the role
- Therefore, we focus on empowering them with data-driven solutions.
- The Senior Full-Stack Machine Learning Engineer sits within the Insights Business Unit, which serves as Inovalon's central AI and machine learning hub.
- In this role, you will contribute to both classical machine learning and generative AI applications, including LLM-based and agentic solutions.
Responsibilities
- Build and maintain cloud-native solutions on AWS using containerized architectures (Docker, Kubernetes) to support scalable model serving and data pipelines.
- Own and contribute to the full Model Development Lifecycle (MDLC), including dataset versioning, model versioning, model registry management, and model evaluation frameworks.
- Develop and integrate Python-based ML components that work seamlessly with existing product platforms across multiple business units.
- Collaborate with AI Product Managers across the Insights BU and partner business units (Provider, Payer, Pharmacy) to translate business needs into AI solutions.
- participate in code and design reviews.
- Support integration of frontend components into ML-powered features where applicable.
- Frontend development skills to support full-stack ML feature work.
Requirements
- Expert-level Python proficiency
- Demonstrated experience with generative AI, LLMs, or agentic application development.
- Proficiency with PyTorch and neural network architectures.
- Practical knowledge of the Model Development Lifecycle (MDLC): dataset versioning, model versioning, model registry, and model evaluation.
- Containerization experience with Docker and/or Kubernetes.
- Experience with database technologies (SQL or NoSQL)
- familiarity with data pipeline tooling.
- Experience with database technologies (SQL or NoSQL); familiarity with data pipeline tooling.
- Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related technical field required.
- Required
- Minimum 5 years of software development experience with a strong foundation in machine learning fundamentals and model training.
- Python is the team's primary language and is the highest-priority technical requirement.
- Hands-on experience building and deploying classical ML models in production using scikit-learn.
- AWS cloud experience, including deploying and managing cloud-native workloads.
- Strong problem-solving ability
- demonstrated capacity to work independently and take ownership of complex technical challenges.
- Daily usage of AI-assisted coding tools (e.g., Claude Code, GitHub Copilot, or similar) as part of standard development workflow.
- Preferred
Compensation
- Inovalon Offers a Competitive Salary and Benefits Package
Benefits
- Healthcare domain experience or exposure to HIPAA-regulated environments.
- Master's degree or PhD in Computer Science, Machine Learning, or equivalent practical experience preferred.
- Design, train, and deploy machine learning models spanning classical ML (classification, regression, clustering, time-series) and generative AI use cases including LLM-based and agentic applications.
- Apply neural networks and deep learning techniques using PyTorch for appropriate use cases alongside scikit-learn-based classical approaches.
- The actual base pay offered may vary depending on multiple factors including, but not limited to, job-related knowledge/skills, experience, business needs, geographical location, and internal equity.
Company info
- Leverage AI coding tools (such as Claude Code or equivalent) as part of your daily development workflow to improve velocity and code quality.
Visa & Work Authorization
- This position is not eligible for immigration sponsorship (e.g. H-1B, TN, or E-3).
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This listing is sourced directly from Inovalon's careers page and normalized into a canonical job model.