Job Description
Mayo Clinic created Lucem Health in 2021 with its investor partners Rally Ventures and Commure (a General Catalyst company), along with the founding team. Our mission is to revolutionize care delivery through the practical application of clinical AI; our vision is a world in which clinicians detect problems before they become life threatening and patients get world class care, everywhere.
Our platform connects patient data from varied sources with powerful, clinically focused AI algorithms and other advanced tools. We deliver the insights generated by these tools seamlessly into clinical workflow: to the right stakeholder, in the right place, in the right context, at the right time. And we help clinicians and other stakeholders engage with, understand, trust, and adopt these tools so they see them as valuable partners that support better care and outcomes for patients. Lucem provides AI-enabled software solutions that deliver specific and measurable clinical value propositions and financial benefits to our customers.
Position Summary
At our core, we transform clinical artificial intelligence into practical, real-world solutions that improve care delivery and operational efficiency for healthcare providers. As a Software Engineer (MLOps) on our model development team, you will help bridge the gap between clinical machine learning and production-grade software engineering. Reporting directly to the Development Manager, you will contribute to the automated infrastructure, deployment pipelines, and operational guardrails required to scale our clinical AI portfolio. Your primary mission is to help build a highly automated “model factory” that ensures our models are securely deployed, continuously monitored for drift, and seamlessly integrated into production Google Cloud environments. If you are a rigorous software engineer who wants to apply your skills directly to improving patient outcomes, this is the role for you.
Responsibilities:
- Collaborate with clinical experts, data scientists, and software developers to translate business and clinical needs into robust, scalable MLOps solutions.
- Design, build, and optimize end-to-end automated pipelines for clinical model training, retrospective validation, deployment, and monitoring.
- Work with data scientists to implement automated model evaluation, benchmarking, and retraining logic, ensuring deep alignment between model performance metrics and production guardrails.
- Collaborate with the Data Engineering team to define and consume standardized datasets from common clinical data models (e.g., OMOP), ensuring models are fed with highly structured, clean clinical data.
- Maintain and expand our automated model deployment pipeline, including all model, code, data artifacts, workflows, and repositories, to enable seamless, “push-button” production deployments and automated retrospective validations.
- Implement automated monitoring, logging, and alerting systems to track model inputs, output feature drift, and operational latency in Google Cloud Platform (GCP) production environments.
- Manage and optimize cloud infrastructure for machine learning workloads specifically on Google Cloud Platform (GCP).
- Develop and maintain infrastructure as code using tools like Terraform.
- Create and maintain clear architecture diagrams to document, justify, and communicate decisions for new MLOps infrastructure and patterns.
- Write and maintain highly performant, production-grade applications and automation scripts for core MLOps services.
- Contribute to robust clinical model governance practices, including drafting and automating model documentation templates (such as CHAI Model Cards) to track clinical bias, model performance parameters, and data drift over time.
- Stay updated with the latest trends in MLOps, deployment patterns, and healthcare-focused model governance.
Qualifications:
- Bachelor’s degree or higher in computer science, data science, statistics, biomedical engineering, or a related field (or equivalent practical experience).
- 3+ years of professional experience in software engineering, with a focus on machine learning operations (MLOps) or production platform engineering.
- Proficiency in Python, with exposure to common data science libraries and frameworks (such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
- Experience deploying, hosting, and managing the deployment pipeline for AI/ML models on Google Cloud Platform (GCP).
- Familiarity with clinical data standards (such as OMOP, FHIR, DICOM) and familiarity with clinical terminologies (such as ICD, SNOMED, LOINC).
- Experience working in a PHI-regulated environment or HIPAA-compliant secure cloud environment.
- Strong analytical, problem-solving, and communication skills, with a passion for improving healthcare outcomes.
- Familiarity or professional experience with Golang for systems development and backend services is preferred.
- Practical experience or a background in machine learning model development is preferred, particularly with a strong understanding of how to evaluate model performance, detect data leakage, and diagnose training-versus-production discrepancies.