QA Engineer

About Reality Defender

Reality Defender provides accurate, multi-modal AI-generated media detection solutions to enable enterprises and governments to identify and prevent fraud, disinformation, and harmful deepfakes in real time. A Y Combinator graduate, Comcast NBCUniversal LIFT Labs alumni, and backed by DCVC, Reality Defender is the first company to pioneer multi-modal and multi-model detection of AI-generated media. Our web app and platform-agnostic API built by our research-forward team ensures that our customers can swiftly and securely mitigate fraud and cybersecurity risks in real time with a frictionless, robust solution.

Youtube: Reality Defender Wins RSA Most Innovative Startup

Why we stand out:

• Our best-in-class accuracy is derived from our sole, research-backed mission and use of multiple models per modality

• We can detect AI-generated fraud and disinformation in near- or real time across all modalities including audio, video, image, and text.

• Our platform is designed for ease of use, featuring a versatile API that integrates seamlessly with any system, an intuitive drag-and-drop web application for quick ad hoc analysis, and platform-agnostic real-time audio detection tailored for call center deployments.

• We’re privacy first, ensuring the strongest standards of compliance and keeping customer data away from the training of our detection models.

Role and Responsibilities

• Assess reliability of AI/Deep Learning models via QA testing

• Interact with state-of-the-art generative AI techniques to create challenging test data for AI/Deep Learning models

• Prepare generated data and reports of testing results for further investigation from AI teams

About You

• Bachelor’s Computer Science, Electrical Engineering, or related field Experience using image, video, and audio generation platforms (Midjourney, Stable Diffusion, ElevenLabs, etc.)

• Proficiency coding with shell scripts and Python and basics of deep learning Good communication skills, self-starter and ability to interface across teams internally.

• Curious and experimental mindset for discovering model failure modes.