Machine Learning Engineer

Summary

UST is a technology and AI solutions company serving clients across industries, including healthcare. The Machine Learning Engineer will design, build, validate, deploy, and monitor machine learning models for healthcare payer use cases while developing Databricks feature engineering pipelines and applying security and compliance practices to sensitive data.

Responsibilities

• 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

Skills

• 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, TensorFlow, 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
• 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
• 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
• 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

Qualifications

Must Haves

• 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, TensorFlow, 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
• 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

Nice to Haves

• 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
• 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

Benefits

• Full-time, regular employees accrue a minimum of 10 days of paid vacation per year
• Full-time, regular employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year)
• Full-time, regular employees receive 10 paid holidays
• Full-time, regular employees are eligible for paid bereavement leave and jury duty
• Full-time, regular employees are eligible to participate in the Company’s 401(k) Retirement Plan with employer matching
• Full-time, regular employees and their dependents residing in the US are eligible for medical, dental, and vision insurance
• Company-paid basic life insurance for full-time, regular employees
• Company-paid accidental death and disability insurance for full-time, regular employees
• Company-paid short- and long-term disability benefits for full-time, regular employees
• Regular employees may purchase additional voluntary short-term disability benefits
• Regular employees may participate in a Health Savings Account (HSA)
• Regular employees may participate in a Flexible Spending Account (FSA) for healthcare, dependent child care, and/or commuting expenses as allowable under IRS guidelines
• Part-time employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year)
• Part-time employees are eligible to participate in the Company’s 401(k) Retirement Plan with employer matching
• Full-time temporary employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year)
• Full-time temporary employees are eligible to participate in the Company’s 401(k) program with employer matching
• Full-time temporary employees and their dependents residing in the US are eligible for medical, dental, and vision insurance
• Part-time temporary employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year)
• All US employees who work in a state or locality with more generous paid sick leave benefits than specified here will receive the benefit of those sick leave laws