Machine Learning Engineer

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 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.

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

• 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

About You

• 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

  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

Compensation Range: $150K - $220K

Originally posted on Himalayas