Applied AI Engineer (Core)

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

Compensation Range: $160K - $200K