Machine Learning Engineer

1 day ago

Remote, United States or Pittsburgh, PA, USAMid Level

Responsibilities

Build and maintain machine-learning CI/CD pipelines for automated testing, model deployment, and version control.
Deploy machine-learning models as scalable APIs and microservices that meet clinical performance and latency requirements.
Implement monitoring for model performance, data drift, and production system health.
Develop and optimize ETL pipelines that transform FHIR and HL7 healthcare data for training and inference.
Build and maintain feature stores and data layers for consistency between training and production environments.
Integrate machine-learning outputs into core healthcare applications with backend teams.
Write clean, maintainable, documented Python code and participate in code reviews.
Use Docker and Kubernetes to package and orchestrate machine-learning workloads.
Follow HIPAA and HITRUST security and compliance standards for data handling and deployments.

Bachelor’s or master’s degree in Computer Science, Software Engineering, or Data Engineering.
Four years of professional software engineering or data engineering experience, including at least two years focused on machine-learning production environments.
Two years of experience with Python and SQL.
Knowledge of a compiled language such as Go or Java.
Two years of experience with at least one major cloud provider—AWS, Azure, or GCP—and Docker.
Two years of experience with PyTorch or Scikit-learn and MLOps tools such as Airflow, Prefect, BentoML, or Kubeflow.
Two years of experience with data-processing frameworks such as Pandas, Spark, or dbt.
Familiarity with deploying large language models or using LangChain is an additional qualification.
Experience in a regulated environment and understanding of API design and microservices architecture are additional qualifications.
Must be legally authorized to work in the country of employment without employment-visa sponsorship.

Remote work location with up to 10% domestic travel.
Company-paid and coordinated onsite onboarding during the initial employment days for applicable hires starting October 1, 2025 or later.
Benefits may include medical, dental and vision coverage, health savings accounts, flexible spending accounts, disability benefits, life insurance, voluntary benefits, paid absences, and retirement benefits.