Applied Scientist Intern (Audio)

1 Month ago • All levels • Audio • $60,000 PA - $96,000 PA

Job Summary

Job Description

Reality Defender is seeking an Applied Scientist Intern with a focus on audio. The intern will investigate open-source audio deepfake datasets, utilize Multimodal LLMs to create feature representations of this data, and analyze trends related to bias, fairness, and classification difficulty. This role involves collaboration with scientists and engineers across the organization. The ideal candidate is currently pursuing a PhD in deep learning, computer vision, speech/audio processing, or a related field, has a strong publication record in reputable AI venues, and possesses excellent data analysis skills. Experience with Multimodal LLMs and Python programming with PyTorch is required, with audio model experience being a plus.
Must have:
  • PhD program enrollment in deep learning, computer vision, speech/audio processing, or related field
  • Implemented/published in AI research venues (CVPR, ICLR, Interspeech)
  • Strong skills in exploratory data analysis techniques
  • Experience with Multimodal LLMs
  • 1+ years of Python programming experience
  • Model building experience in PyTorch
  • Team player with positive attitude and good communication skills
Good to have:
  • Experience with audio models (e.g., HuBERT/wav2vec)

Job Details

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.

Responsibilities

  • Investigate the composition of open source audio deepfake datasets

  • Use Multimodal LLMs to create rich feature representations of audio deepfake data

  • Use feature representations of audio deepfake datasets to identify data trends as they relate to measures of bias, fairness, and difficulty of classification between real and fake data.

  • Collaborate with scientists and engineers across the organization

About You

  • Currently enrolled in a PhD program in deep learning, computer vision, speech/audio processing, or a related field

  • Implemented and/or published peer-reviewed papers in reputable AI research venues such as CVPR, ICLR, Interspeech

  • Strong skills and intuition with exploratory data analysis techniques such as dimensionality reduction and clustering

  • Experience with Multimodal LLMs and an understanding of their architectures

  • Have 1+ years of programming experience in Python and model building in PyTorch; experience with audio models, e.g. HuBERT/wav2vec, would be a plus but not essential.

  • Team player with a positive attitude and good communication skills

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