ML Engineer - Solventum

Job Description:

ML Engineer

3M Health Care is now Solventum

At Solventum , we enable better, smarter, safer healthcare to improve lives. As a new company with a long legacy of creating breakthrough solutions for our customers’ toughest challenges, we pioneer game-changing innovations at the intersection of health, material and data science that change patients' lives for the better while enabling healthcare professionals to perform at their best. Because people, and their wellbeing, are at the heart of every scientific advancement we pursue.

We partner closely with the brightest minds in healthcare to ensure that every solution we create melds the latest technology with compassion and empathy. Because at Solventum , we never stop solving for you.

The Impact You’ll Make in this Role

As an ML Engineer , you will be responsible for building and maintaining the pipelines that power AI in our Healthcare Information Systems (HIS). We are looking for a practical, detail-oriented engineer who is passionate about MLOps, data reliability, and production stability.

In this role, you won’t just be building models; you will be ensuring those models work reliably in the real world. You will help bridge the gap between data science and software engineering by implementing automated workflows, managing cloud infrastructure, and ensuring our AI services are secure and scalable.

Key Responsibilities

1. MLOps & Deployment

Pipeline Development: Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git).

Model Serving: Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use.

Monitoring: Implement basic monitoring tools to track model performance, data drift, and system health in production.

2. Data Engineering & Integration

Data Pipelines: Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference.

Feature Management: Help build and maintain feature stores and data layers that ensure consistency between training and production environments.

System Integration: Work closely with backend teams to integrate ML outputs into our core healthcare applications.

3. Engineering Best Practices

Code Quality: Write clean, maintainable, and well-documented Python code. Participate in code reviews to ensure system reliability.

Containerization: Use Docker and Kubernetes to package and orchestrate ML workloads across different environments.

Security & Compliance: Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards.

Your Skills and Ex