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Reality Defender in New York tests APIs, SDKs, a web app and live real-time integrations to flag AI-generated deepfakes before they reach critical systems. This role emphasizes robust automation and precise bug reporting to support rapid, reliable releases.
The QA Engineer collaborates with engineering, learns model behavior, and ensures edge cases and timing are carefully considered during testing.
A Quality Assurance Engineer here typically needs to test APIs, SDKs, a web app and live real time integrations, and to build automation that keeps releases fast and safe. A strong candidate brings hands on test automation, careful API and integration testing, and the habit of turning manual checks into reliable scripts. Because the product flags deepfakes in live calls and video, close attention to edge cases and timing matters.
AI is changing QA by drafting test cases, generating scripts and spotting patterns in failures, which speeds up routine work. What stays human is judging what is worth testing, deciding whether a flagged deepfake result is actually correct, and owning release confidence. The role sits close to AI output, so the tester becomes a check on the AI itself.
This role builds AI experience through testing detection features on live calls, meetings, images and video, where the tester learns how model output behaves and fails. Skills grow by writing automated checks for API and SDK responses, including cases where AI generated media should and should not be flagged. Working alongside the engineering team on real time integrations adds practical judgment about model behavior.
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
A New York company whose API and SDKs, per the company, scan live calls, meetings, images and video in real time to flag AI-generated deepfakes before they reach a bank, call centre or government system.