Solventum

ML Engineer

Remote Machine Learning Engineer role with clear candidate location fit.

PostedJun 20, 2026

Eligible countries1 accepted country

Seniority signalSenior

Work settingRemote

Accepted candidate locations

USA

Role overview

Readable role content extracted into sections for faster review.

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.

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.

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.

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field.
3–5 years of professional experience in software engineering or data engineering, with at least 2 years focused on machine learning production environments.

Programming: Strong proficiency in Python and familiarity with SQL. Knowledge of a compiled language (like Go or Java) is a plus.
Cloud & Infrastructure: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and containerization (Docker).
ML Tools: Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow).
Data Tools: Experience with data processing frameworks (like Pandas, Spark, or dbt).

Familiarity with deploying Large Language Models (LLMs) or using frameworks like LangChain.
Experience working in a regulated environment (Healthcare, Finance, etc.).
Understanding of API design and microservices architecture.