Among software quality assurance analysts and testers, AI is already used for a large share of the tasks. Measured exposure for software quality assurance analysts and testers: 52% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

Senior Quality Automation Engineer

About Authenticx Authenticx is a fast-growing, venture-backed, enterprise AI company transforming healthcare through conversation intelligence. We help healthcare organizations uncover insights hidden in millions of customer interactions to improve patient, member, and business outcomes.

Role Summary The Senior Quality Automation Engineer is a hands-on role responsible for designing, building, maintaining, and integrating automated tests across Authenticx's healthcare conversation intelligence platform. You will translate customer workflows, product requirements, user personas, and healthcare realities into automated coverage that protects the experiences that matter most. You will own automation for critical product areas and prioritize coverage based on customer impact, product risk, release frequency, and business value. You will partner with Product, Engineering, Data Science, Implementation, and Customer teams to improve product quality, testability, and release confidence. We are looking for someone who brings strong technical judgment, product sense, curiosity, and ownership to quality engineering in a fast-paced startup environment.

What You'll Do Build Automation Around Real Customer Use Cases • Learn how Authenticx customers use the platform across healthcare quality, coaching, compliance, operations, analytics, and customer experience workflows. • Translate workflows, acceptance criteria, production issues, and known risk areas into meaningful automated test scenarios. • Determine what should be automated, which testing layer is most appropriate, and the level of coverage needed. • Balance UI, API, integration, and data-level testing to create reliable and maintainable coverage.

Build and Maintain Automated Coverage • Design, write, maintain, and troubleshoot automated tests for UI workflows, APIs, backend services, data pipelines, integrations, reporting, and AI-powered features. • Build and maintain smoke, regression, acceptance, integration, and end-to-end test suites. • Use Playwright to automate critical user journeys and product workflows. • Use Bruno/Postman to validate APIs, payloads, integrations, authentication, error handling, and backend behavior. • Use SQL to validate data accuracy, workflow integrity, processing outcomes, reporting outputs, and customer-facing analytics. • Build reusable automation utilities, fixtures, helpers, and test data patterns. • Integrate automated tests into CI/CD pipelines to provide earlier and more reliable quality signals. • Investigate flaky tests, environmental issues, timing problems, and test-data failures, and distinguish them from true product defects. • Identify high-risk coverage gaps and build automation to address them.

Partner Cross-Functionally to Improve Quality • Work with Product, Engineering, Data Science, Implementation, and Customer teams to understand requirements, risks, and real-world usage. • Partner with developers to improve testability, observability, logging, test data setup, and API contracts. • Use logs, monitoring tools, database queries, API responses, and product data to investigate failures and validate system behavior. • Communicate defects, automation gaps, and release risks clearly. • Share automation patterns and testing practices that strengthen the broader quality engineering discipline.

Validate AI-Powered Product Experiences • Build automated validation for classifications, model outputs, prompt and configuration changes, structured responses, extraction workflows, and downstream behavior. • Develop repeatable test scenarios for deterministic and semi-deterministic AI behaviors. • Validate expected outputs, required fields, edge cases, failure conditions, and downstream workflow impacts. • Use representative test data and known expected outcomes to detect regressions. • Partner with Data Science and Engineering to improve the testability and regression coverage of AI-powered features.

Required Qualifications • 2+ years in test automation, SDET, or software engineering, with hands on ownership of automated suites • Production level coding in at least one of TypeScript, JavaScript, C#, or Python • Playwright or equivalent modern UI automation on complex web applications • REST API testing (Bruno, Postman, or code based), covering authentication, payload validation, and error handling • SQL proficiency to validate data integrity and processing outcomes • CI/CD integration, including failure triage, flaky test diagnosis, and test data management • Ability to assess product risk and choose the right test layer (UI, API, or data) without having every case defined for you • Experience validating asynchronous, multi service workflows

Preferred Qualifications • C#/.NET, PostgreSQL, and Azure • Experience testing LLM or ML features using representative datasets, expected outcomes, and threshold based regression checks • Ingestion pipelines, event driven systems, and third party integrations • Healthcare, contact center, or regulated environment experience • AI assisted development tools for test creation and debugging