Metric Bio
Machine Learning Engineer - Digital Health
Metric Bio, Sonoma, California, United States, 95476
Join a cutting-edge digital health company that is harnessing the power of AI to revolutionize patient care and healthcare delivery. We are on the lookout for a
Machine Learning Engineer
who is eager to tackle innovative challenges using advanced ML techniques within the healthcare sector. This is your chance to work with a modern tech stack alongside a team that prioritizes collaboration, technical expertise, and a mission-driven approach. Key Responsibilities: Design, train, and optimize machine learning models tailored for healthcare applications, including natural language processing, patient risk scoring, and workflow automation. Create and manage production-quality ML pipelines utilizing MLOps tools (such as MLflow, Kubeflow, or SageMaker). Collaborate with software engineers, data scientists, and healthcare professionals to seamlessly integrate ML models into scalable production environments. Contribute actively to the engineering efforts across the company’s technical stack: Python/Django, Vue, Kubernetes, and GCP. Ensure reliability, fairness, and compliance of models with healthcare regulations (including HIPAA and HITRUST). Engage in code reviews, pair programming, and architecture discussions to foster a collaborative engineering culture. Qualifications: 3-6 years of experience as a Machine Learning Engineer, Applied ML Scientist, or a related role. Proficient programming skills in Python with strong experience in frameworks like TensorFlow, PyTorch, and Scikit-learn. Practical experience deploying ML models in production settings. Cloud experience, preferably with GCP; familiarity with Kubernetes and containerization is a plus. Understanding of healthcare data formats (FHIR, HL7) or experience with digital health systems is highly preferred. Exceptional communication and problem-solving skills, with the ability to collaborate across both technical and clinical teams. A genuine passion for leveraging AI to enhance patient outcomes and improve healthcare delivery.
Machine Learning Engineer
who is eager to tackle innovative challenges using advanced ML techniques within the healthcare sector. This is your chance to work with a modern tech stack alongside a team that prioritizes collaboration, technical expertise, and a mission-driven approach. Key Responsibilities: Design, train, and optimize machine learning models tailored for healthcare applications, including natural language processing, patient risk scoring, and workflow automation. Create and manage production-quality ML pipelines utilizing MLOps tools (such as MLflow, Kubeflow, or SageMaker). Collaborate with software engineers, data scientists, and healthcare professionals to seamlessly integrate ML models into scalable production environments. Contribute actively to the engineering efforts across the company’s technical stack: Python/Django, Vue, Kubernetes, and GCP. Ensure reliability, fairness, and compliance of models with healthcare regulations (including HIPAA and HITRUST). Engage in code reviews, pair programming, and architecture discussions to foster a collaborative engineering culture. Qualifications: 3-6 years of experience as a Machine Learning Engineer, Applied ML Scientist, or a related role. Proficient programming skills in Python with strong experience in frameworks like TensorFlow, PyTorch, and Scikit-learn. Practical experience deploying ML models in production settings. Cloud experience, preferably with GCP; familiarity with Kubernetes and containerization is a plus. Understanding of healthcare data formats (FHIR, HL7) or experience with digital health systems is highly preferred. Exceptional communication and problem-solving skills, with the ability to collaborate across both technical and clinical teams. A genuine passion for leveraging AI to enhance patient outcomes and improve healthcare delivery.