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 tdhe 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.
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.
Train/finetune deep learning models in PyTorch on new datasets and per client requirements
Model monitoring and quality assurance for deployed models
ML workflow automation and continuous integration/continuous delivery (CI/CD) for client-facing models
Adopt standard model optimization/compression methods for inference speed-up
Implement model obfuscation and vulnerability checks
Collaborate with both AI and Engineering teams for model/infrastructure needs and performance guidance
Masters or PhD in Computer Science with specialization in machine learning/deep learning (ML/DL)
2+ years coding experience in Python; Strong programming skills required
2+ years industry experience with model training/finetuning in PyTorch
[Preferred] Experience finetuning large foundation models, e.g. wav2vec, HuBERT for downstream classification
Experience with automated testing and CI/CD concepts in machine learning workflow
Strong foundation in machine learning and data science
Good communication and inter-personal skills, comfortable with client-facing responsibilities
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