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AI Research Engineer (Staff) - Engineering Transformation
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AI Summary
Design, build, and deploy ML models for healthcare payer use cases (claims, clinical, provider analytics). Requires 2–4 years of ML engineering experience, Python proficiency, and Databricks/AWS expertise. Focus on feature engineering, model deployment, and compliance with HIPAA.
Key Highlights
Full lifecycle ML engineering (feature engineering to deployment) on Databricks and AWS
HIPAA compliance and secure handling of PHI/PII data
Collaboration with data engineers, AI developers, and product teams
Key Responsibilities
Design and validate ML models for healthcare payer use cases (claims risk scoring, clinical outcomes, provider analytics).
Develop and maintain feature engineering pipelines on Databricks, including feature stores and transformations.
Deploy and monitor ML models in production on Databricks, ensuring performance, data drift detection, and remediation.
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Technical Skills Required
Benefits & Perks
10 days paid vacation (full-time)
6 days paid sick leave (full-time)
10 paid holidays
401(k) retirement plan with employer matching
Health Savings Account (HSA) eligibility
Flexible Spending Account (FSA) eligibility
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Nice to Have
Databricks Certification (Associate/Professional).
AWS Certification (ML Specialty/Solutions Architect).
Prior experience in health insurance or regulated industries (HIPAA/SOC 2 exposure).
MLOps tooling experience (MLflow, model registries, CI/CD for ML).
Job Description
The Role
UST is searching for a Machine Learning Engineer to design, build, and deploy ML models that drive insights and automation across our healthcare payer business — including claims, clinical, provider, and Medicare Advantage data.
What You’ll Do
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