Location: New York,NY, USA
About Reality DefenderReality 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 StartupWhy we stand out:Our best-in-class accuracy is derived from our sole, research-backed mission and use of multiple models per modalityWe 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 ResponsibilitiesAssess reliability of AI/Deep Learning models via QA testingInteract with state-of-the-art generative AI techniques to create challenging test data for AI/Deep Learning modelsPrepare generated data and reports of testing results for further investigation from AI teamsAbout YouBachelor'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.