A growing AI and Data Science team at a healthcare-focused company is looking for a Senior Machine Learning Engineer to take ownership of complex, enterprise-scale ML initiatives. This is a W2 contract role for work-authorized candidates (no visa sponsorship available). You'll work in a fast-paced environment building production-grade ML solutions that directly impact patient outcomes and healthcare operations — with a strong emphasis on compliance, reliability, and end-to-end ownership.
🏢 About [Company]
About the Role
🎯 The Role
A growing AI and Data Science team at a healthcare-focused company is looking for a Senior Machine Learning Engineer to take ownership of complex, enterprise-scale ML initiatives. This is a W2 contract role for work-authorized candidates (no visa sponsorship available). You'll work in a fast-paced environment building production-grade ML solutions that directly impact patient outcomes and healthcare operations — with a strong emphasis on compliance, reliability, and end-to-end ownership.
✅ Key Responsibilities
Own the full ML lifecycle: data ingestion, feature engineering, model training, evaluation, deployment, monitoring, retraining, and maintenance.
Design and build scalable, production-ready ML systems with high availability, performance, and reliability.
Develop and maintain MLOps pipelines — including CI/CD, model registry, feature stores, automated deployment, monitoring, and rollback strategies.
Monitor production models for drift (model, data, accuracy degradation) and overall system health.
Build and integrate REST APIs to connect ML services into enterprise cloud applications.
Optimize models for latency, scalability, reliability, and operational cost.
Provide technical leadership on AI/ML initiatives across the organization.
Collaborate with Data Engineers, Software Engineers, Product Managers, Clinical teams, and business stakeholders.
Ensure strict compliance with HIPAA, PHI, PII, and enterprise security standards throughout all ML workflows.
📌 Required Qualifications
8+ years of professional software engineering and machine learning experience.
Healthcare domain experience is mandatory — including HIPAA compliance and handling of sensitive patient data (PHI/PII).
Demonstrated ownership of end-to-end ML lifecycle from data preparation through deployment, monitoring, and retraining.
Experience designing and operating production-grade ML systems at scale.
Hands-on MLOps: CI/CD pipelines, model registry, feature stores, automated deployment, monitoring, and rollback.
