Machine Learning Engineer

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.

• Design, build, and validate machine learning models addressing healthcare payer use cases (e.g., claims risk scoring, clinical outcome prediction, provider network analytics, Medicare Advantage risk adjustment).
• Develop and maintain feature engineering pipelines on Databricks, including feature stores, transformations, and reusable feature sets for model training and inference.
• Train, tune, and evaluate models using standard ML frameworks, tracking experiments and managing the model lifecycle (versioning, registry, lineage).
• Deploy models to production on Databricks (batch and/or real-time serving), integrating with downstream applications and monitoring systems.
• Monitor deployed models for performance, data drift, and degradation, and implement retraining or remediation workflows as needed.
• Apply data security and privacy best practices when handling PHI/PII and other sensitive healthcare data, in line with HIPAA and enterprise compliance requirements.
• Partner with data engineers, AI/agentic systems developers, product owners, and compliance stakeholders to translate business problems into ML solutions.
• Write clean, well-tested, production-quality code and contribute to shared ML tooling, standards, and documentation.

This position description identifies the responsibilities and tasks typically associated with the performance of the position. Other relevant essential functions may be required.

• 2-4 years of hands-on experience in machine learning engineering or applied data science, covering the full lifecycle from feature development through model deployment.
• Strong proficiency in Python and common ML libraries/frameworks (e.g., scikit-learn, PyTorch, Tensor Flow, MLflow).
• Practical experience building, training, and deploying models on Databricks, including feature engineering, experiment tracking, and model serving/ deployment on the platform.
• Working knowledge of AWS as the underlying cloud infrastructure for data and ML workloads.
• Solid understanding of data security fundamentals - encryption, access controls, and secure handling of sensitive data - with comfort operating in a regulated enterprise environment.
• Healthcare background is a strong plus - familiarity with claims, clinical, provider, or Medicare Advantage data is valued but not required for strong ML engineering candidates willing to learn the domain.
• Solid software engineering fundamentals: version control, testing, CI/CD, and code review practices.
• Strong analytical and communication skills, with the ability to explain model behavior and tradeoffs to both technical and non-technical stakeholders.

• Databricks Certification (e.g., Databricks Certified Machine Learning Associate/Professional). AWS Certification (e.g., AWS Certified Machine Learning - Specialty, AWS Certified Solutions Architect).
• Prior experience in health insurance, health plans, or another regulated industry, with exposure to HIPAA, SOC 2, or similar compliance frameworks.
• Experience with MLOps tooling (MLflow, model registries, CI/CD for ML) and monitoring/observability for production models.

Compensation can differ depending on factors including but not limited to the specific office location, role, skill set, education, and level of experience. UST provides a reasonable range of compensation for roles that may be hired in various U.S. markets as set forth below.

Compensation Range: $82,000-$123,000

Since 1999, UST has worked side by side with the world's best companies to make a powerful impact through transformation. Headquartered in the United States, UST is powered by technology, driven by AI, inspired by people, and led by our purpose, we partner with our clients from design to operation. Our AI-driven digital solutions, proprietary platforms, engineering, R&D, products, and innovation ecosystem turn core challenges into impactful, disruptive business outcomes.

With deep industry knowledge and a future-ready mindset, we infuse expertise, innovation, and agility into our clients' organizations-delivering measurable value and positive lasting change for them, their customers, and communities around the world. Together, with 30,000+ employees in 30+…