Senior AI/ML Engineer

About Company:

The healthcare industry is the next great frontier of opportunity for software development, and Health Catalyst is one of the most dynamic and influential companies in this space. We are working on solving national-level healthcare problems, and this is your chance to improve the lives of millions of people, including your family and friends. Health Catalyst is a fast-growing company that values smart, hardworking, and humble individuals. Each product team is a small, mission-critical team focused on developing innovative tools to support Catalyst’s mission to improve healthcare performance, cost, and quality.

Health Catalyst is expanding and maintains a large suite of Improvement Apps that contribute to healthcare analytics and process improvement solutions. This includes products that manage the care of health system populations, better serve patients at the point of care, reduce health system costs, and reduce clinician workload.

Job Summary:

As a senior AI/ML engineer, you will be working with diverse Apps, software engineering team designing, developing, and maintaining various platforms that serve internal HCAT team members, clinicians, and patients. You will rely on Test-Driven Development to safely enhance and refactor our system, shipping production code multiple times per week. And you will go to bed each night with the comfort that your code is improving outcomes for patients.

If you love…

Help drive clarity and prototype individual features or problems

Knowledge of architecture patterns and the ability to design and complete features / tasks that are 50-60% well defined.

Can discern where gaps can be filled in without consulting a Product Manager or another programmer and can judge when a consultation is needed.

Work is reviewed with the occasional need for material direction or implementation changes

Seeks and provides guidance via PR reviews, pair-programming and other interactions with Engineers and Product Managers

It is second nature to develop high code quality standards balanced with the needs of real-world customer timelines.

Possesses a passion and drive to deliver exceptional products and follows established patterns and approaches within existing code bases with ease.

Takes ownership of learning and growth

Capitalizes on internal and external opportunities for learning.

Identifies gaps in knowledge/skills and seeks ways to close those gaps (self-guided learning, pairing, seeking guidance for yourself and developing guidance for less experienced members of the team)

Periodic On Call Rotation

Ability to communicate with Customer Success about customer issues that are escalated to Engineering and help quantify customer impact.

Can Respond quickly to operational emergencies, find short term resolutions and plan long term fixes to avoid similar issues in the future.

What you own in the role:

• Design, develop, and deploy machine learning models and AI solutions for real-world business problems.
• Implement MLOps pipelines to automate model training, testing, deployment, monitoring, and retraining.
• Collaborate with data engineering teams to ensure clean, reliable, and scalable data pipelines.
• Optimize model performance and scalability across large datasets and distributed environments.
• Integrate ML models into production systems with APIs and microservices.
• Work with cloud platforms (AWS, Azure, or GCP) to manage compute, storage, and ML services.
• Proficiency in SQL for data manipulation and analysis
• Establish monitoring, logging, and alerting solutions for ML model performance and data drift.
• Contribute to architecture design, technical roadmaps, and best practices in ML engineering.
• Mentor junior team members and contribute to knowledge sharing across the organization.
• Experience with large language models (LLMs), generative AI, or reinforcement learning.
• Knowledge of data versioning tools (DVC, Delta Lake, LakeFS).
• Familiarity with feature stores and advanced monitoring for ML pipelines.

What you bring to this role:

• Bachelor’s/Master’s degree in Computer Science, Data Science, AI/ML, or related field (PhD a plus).
• 6+ years of experience in AI/ML engineering with a proven track record of building and deploying models.
• Strong knowledge of Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, Airflow, etc.).
• Solid understanding of DevOps principles (CI/CD, Docker, Kubernetes, GitOps) in ML workflows.
• Experience with data preprocessing, feature engineering, and model evaluation techniques.
• Knowledge of cloud environments (AWS/GCP/Azure) and cloud-native ML services.