Metric Bio
Metric Bio is recruiting on behalf of a San Franciscobased digital health company that is building an AI-powered platform to transform patient care and healthcare delivery. We are seeking a
Machine Learning Engineer
who is passionate about applying advanced ML techniques to solve complex challenges in healthcare. This is an opportunity to work on a modern stack with a team that values hands-on technical depth, collaboration, and mission-driven impact. Responsibilities: Design, train, and optimize machine learning models for healthcare applications, including natural language processing, patient risk scoring, and workflow automation. Develop and maintain production-grade ML pipelines using MLOps tools (MLflow, Kubeflow, SageMaker, or similar). Collaborate with software engineers, data scientists, and clinicians to integrate ML models into scalable production systems. Contribute hands-on to engineering across the companys technical stack: Python/Django, Vue, Kubernetes, and GCP. Ensure reliability, fairness, and compliance of models with healthcare standards (HIPAA, HITRUST). Participate in code reviews, pair programming, and architecture discussions as part of a collaborative engineering culture. Qualifications: 36 years of experience as a Machine Learning Engineer, Applied ML Scientist, or related role. Strong programming skills in Python and deep familiarity with frameworks such as TensorFlow, PyTorch, and Scikit-learn. Hands-on experience deploying ML models into production environments. Cloud expertise, ideally with GCP; Kubernetes and containerization experience preferred. Knowledge of healthcare data formats (FHIR, HL7) or experience with digital health systems strongly preferred. Excellent communication and problem-solving skills, with the ability to collaborate across technical and clinical teams. Passion for leveraging AI to improve patient outcomes and healthcare delivery.
Machine Learning Engineer
who is passionate about applying advanced ML techniques to solve complex challenges in healthcare. This is an opportunity to work on a modern stack with a team that values hands-on technical depth, collaboration, and mission-driven impact. Responsibilities: Design, train, and optimize machine learning models for healthcare applications, including natural language processing, patient risk scoring, and workflow automation. Develop and maintain production-grade ML pipelines using MLOps tools (MLflow, Kubeflow, SageMaker, or similar). Collaborate with software engineers, data scientists, and clinicians to integrate ML models into scalable production systems. Contribute hands-on to engineering across the companys technical stack: Python/Django, Vue, Kubernetes, and GCP. Ensure reliability, fairness, and compliance of models with healthcare standards (HIPAA, HITRUST). Participate in code reviews, pair programming, and architecture discussions as part of a collaborative engineering culture. Qualifications: 36 years of experience as a Machine Learning Engineer, Applied ML Scientist, or related role. Strong programming skills in Python and deep familiarity with frameworks such as TensorFlow, PyTorch, and Scikit-learn. Hands-on experience deploying ML models into production environments. Cloud expertise, ideally with GCP; Kubernetes and containerization experience preferred. Knowledge of healthcare data formats (FHIR, HL7) or experience with digital health systems strongly preferred. Excellent communication and problem-solving skills, with the ability to collaborate across technical and clinical teams. Passion for leveraging AI to improve patient outcomes and healthcare delivery.