ML Engineer
Role details
Job location
Tech stack
Job description
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
- 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.
- 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.
- 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., Onboarding Requirement: To improve the onboarding experience, you will have an opportunity to meet with your manager and other new employees as part of the Solventum new employee orientation. As a result, new employees hired for this position will be required to travel to a designated company location for on-site onboarding during their initial days of employment. Travel arrangements and related expenses will be coordinated and paid for by the company in accordance with its travel policy. Applies to new hires with a start date of October 1st 2025 or later.
Responsibilities of this position include that corporate policies, procedures and security standards are complied with while performing assigned duties.
Solventum is committed to maintaining the highest standards of integrity and professionalism in our recruitment process. Applicants must remain alert to fraudulent job postings and recruitment schemes that falsely claim to represent Solventum and seek to exploit job seekers.
Please note that all email communications from Solventum regarding job opportunities with the company will be from an email with a domain of @solventum.com. Be wary of unsolicited emails or messages regarding Solventum job opportunities from emails with other email domains.
Please note, Solventum does not expect candidates in this position to perform work in the unincorporated areas of Los Angeles County.
Solventum is an equal opportunity employer. Solventum will not discriminate against any applicant for employment on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status.
Requirements
- 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.
AND * 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).
Additional qualifications that could help you succeed even further in this role include:
- 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.