ABOUT THE ROLE
We are looking for a Manager - QA Automation to lead quality across our AI powered platform and the clinical workflows it serves. This role owns two connected priorities: modern test automation built on Playwright and AI assisted tooling, and a rigorous quality practice for the large language model and multi agent features that support transmission review. You will define how we evaluate AI behavior against clinical accuracy standards, how we automate regression across a complex integration surface, and how we prove production readiness before every release. You will also mentor engineers across the quality function and help set the standard for a single, automation first quality team.
WHAT YOU WILL WORK ON
- Atlas AI and AI generated transmission notes, where clinical accuracy, grounding, and safe language directly affect patient care.
- The clinicfacingwebapplicationand the mobile application used by cardiology care teams every day.
- Integrations with major cardiac device manufacturersand withelectronic health record systems through HL7 and FHIR interfaces.
- The full transmission lifecycle, from ingestion through review, sign off, and delivery, across the clinic, patient, device, and transmission data model.
KEY RESPONSIBILITIES
- Ownend to endquality strategy for AI powered features, covering tool calling, grounding, hallucination detection, guardrails, and conversational reliability.
- Design evaluation datasets and scoring criteria that measure AI generated clinical notes against device specialist review and defined accuracy targets.
- Build guardrail and safety testsuitesfor a healthcare context, covering PHI and PII protection, grounding of clinical statements to actual device data, prevention ofout of scopemedical guidance, prompt injection, jailbreak attempts, and policy compliance.
- Validate transmission ingestion, review, and sign off workflows across multiple device manufacturers and transmission types.
- Test electronic health record integrations and data mapping, confirming accuracy and integrity across HL7 and FHIR data flows.
- Build observability dashboards thatmonitortraces, spans, evaluation scores, and model quality across releases, and investigate regressions to their root cause.
- Design andmaintainPlaywright automation frameworks, using AIassistedtooling to accelerate selector generation and improve reliability.
- Extend automated coverage across UI, API, and backend data layers, integrated with CI/CD for scheduled regression and smoke testing.
- Perform API testing and write SQL for data validation across complex clinical and billing workflows.
- Support HIPAA aligned test practices, including the use of de identified or synthetic data innon-productionenvironments, and help produce quality evidence for audit readiness under frameworks such as SOC 2 and HITRUST.
- Partner with engineering, product, and clinical stakeholders to resolve integration, prompt, and conversational quality issues before production.
- Define quality metrics and reporting for leadership, and mentor quality engineers on AI evaluation, modern automation practices, and test strategy.
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