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Applied AI Engineer (Core)

Remote:

Full Remote

Contract:

Full time

Experience:

Mid-level (2-5 years)

Work from:

New York (USA), United States

Offer summary

Qualifications:

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field, Experience with deep learning architectures (transformers, CNNs), Strong software development skills, Familiarity with PyTorch, ONNX, TensorRT, 2+ years industry experience preparing ML models for production.

Key responsabilities:

Optimize deep learning models for deployment using relevant frameworks
Develop techniques for model quantization and compression
Implement model obfuscation and secure deployments
Collaborate to integrate performance optimization techniques
Analyze and improve existing model architectures

Reality Defender Computer Hardware & Networking Startup https://realitydefender.com/

11 - 50 Employees

See more Reality Defender offers

Job description

Your missions

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