Solventum

ML Engineer

Rol remoto de Machine Learning Engineer con fit claro de ubicación del candidato.

Publicado20 jun 2026

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Señal de senioritySenior

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Estados Unidos

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Actualidad de la fuente20 jun 2026

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Match de stackAWS, Azure

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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.