Machine Learning Engineer
Overview:
In this role, you will design, build, and deploy ML models to derive insights and automation for healthcare payer data. You will work closely with cross-functional partners to translate business problems into ML solutions and maintain production-grade ML tooling. The role focuses on scalable feature engineering, model lifecycle management, and compliant handling of PHI/PII. You will operate in a regulated environment while shaping data-driven outcomes that impact claims, clinical, and Medicare Advantage domains. This is a hands-on, impact-driven position at the intersection of healthcare and AI.Compensation / Benefitspaid vacationpaid sick leavepaid holidays 401(k) with employer matchingmedical, dental, vision insurancelife and disability insurance
Responsibilities:
Design, build, and validate ML models for payer use cases (claims risk scoring, outcome prediction, provider analytics, Medicare Advantage risk adjustment).Develop and maintain feature pipelines on Databricks, including feature stores and reusable feature sets.
Train, tune, and evaluate models; track experiments and manage lifecycle (versioning, registry, lineage).Deploy models to production on Databricks (batch/real-time serving) with integration to downstream systems.
Monitor model performance, drift, and degradation; implement retraining/remediation workflows as needed.
Apply data security and privacy best practices for PHI/PII in line with HIPAA and compliance requirements.
Collaborate with data engineers, AI/system developers, product owners, and compliance stakeholders to translate problems into ML solutions.
Write clean, tested, production-quality code and contribute to shared ML tooling, standards, and documentation.
Key requirements2–4 years of hands-on ML engineering or applied data science (end-to-end lifecycle).Proficiency in Python and ML libraries/frameworks (scikit-learn, PyTorch, TensorFlow, MLflow).Experience building/training/deploying models on Databricks (feature engineering, experiment tracking, model serving).Working knowledge of AWS as cloud infrastructure for data/ML workloads.
Solid understanding of data security fundamentals and operating in a regulated environment.
Healthcare domain familiarity is a plus but not required; willingness to learn the domain.
Software engineering fundamentals: version control, testing, CI/CD, code review.
Strong analytical and communication skills to explain model behavior to diverse stakeholders.strong communicationcross-functional collaborationanalytical thinking
DatabricksPythonscikit-learn