Clinical Systems Analyst

About The Role

Your deep knowledge of clinical systems and healthcare IT is more valuable than ever — and not just in hospitals. At Alignerr, we partner with the world's leading AI research labs to build and train cutting-edge AI models, and we need Clinical Systems Analysts to help us get it right. In this role, you'll apply your real-world expertise in healthcare IT to evaluate, improve, and validate AI-generated content and solutions in the clinical domain. Your experience at the intersection of clinical workflows and technical systems is exactly what it takes to make AI smarter and more reliable in one of the most critical industries on earth.

Evaluation

• Evaluate AI-generated outputs related to clinical systems, healthcare workflows, and hospital IT for accuracy, completeness, and real-world applicability
• Apply your troubleshooting and optimization expertise to assess AI responses involving EHRs, clinical applications, and hospital infrastructure
• Identify errors, gaps, or unrealistic recommendations in AI-generated clinical IT content
• Translate clinical and operational knowledge into structured, actionable feedback that directly improves AI model performance
• Provide clear, well-reasoned evaluations using defined rubrics and guidelines
• Work independently and asynchronously on your own schedule

Who You Are

• Experienced in supporting, administering, or optimizing healthcare IT systems — including EHRs, clinical applications, or hospital infrastructure
• Strong technical troubleshooting and systems thinking skills
• Able to bridge the gap between clinical workflow needs and technical configurations
• Detail-oriented with a commitment to accuracy and quality
• Clear and effective written communicator
• Comfortable working independently on task-based assignments

Nice to Have

• Prior experience with data annotation, data quality assessment, or AI evaluation workflows
• Background in health informatics, clinical informatics, or healthcare interoperability standards
• Familiarity with HL7, FHIR, or other healthcare data standards