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Applied AI Engineer (Core)

Reality Defender
Full-time
On-site
Manhattan, New York, United States

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

  • Optimize deep learning models for deployment using Pytorch, ONNX, TensorRT, and other relevant frameworks.

  • Develop and implement techniques for model quantization and compression to reduce memory footprint and increase inference speed.

  • Develop and implement techniques for model obfuscation and secure deployments.

  • Collaborate with AI researchers and developers to integrate advanced performance optimization techniques into our production systems.

  • Analyze and improve existing model architectures for better efficiency and performance.

  • Interface with production engineering team for assistance with on-prem deployments

About You

  • Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field

  • Experience implementing modern deep learning architectures (transformers, CNNs, etc.)

  • Experience compiling model inference code for deployment

  • Strong software development skills

  • Strong familiarity with machine (deep) learning frameworks such as PyTorch, ONNX, and TensorRT

  • 2+ years industry experience preparing ML models for production