Applied Scientist (Audio Modeling) - ZipRecruiter : Job Details

Applied Scientist (Audio Modeling)

ZipRecruiter

Job Location : New York,NY, USA

Posted on : 2025-07-26T01:31:47Z

Job Description :

Job Description

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, ensure 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 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

  • Explore and conceptualize novel methods to leverage different modalities (e.g., speech, text) for deepfake detection and relevant audio understanding tasks.

  • Perform fundamental and applied research to advance the current state-of-the-art on audio deepfake detection.

  • Build models with generalizability to unseen generative methods. Collaborate with scientists and engineers across the organization.

  • Summarize, publish, and present research findings.

About You

  • Currently enrolled in a PhD program with specialization in machine learning/deep learning, natural language processing, and/or speech processing.

  • 2+ years of experience with training/fine-tuning large models, especially audio models, speech foundation models, multi-modal foundation models.

  • Experience with end-to-end model building pipeline for ML tasks: dataset curation/cleaning, model implementation, benchmarking, and result analysis.

  • Familiarity with distributed multi-GPU training. Prior experience with publications in reputable ML/Audio/NLP research venues, e.g., NeurIPS, Interspeech, ICASSP, ACL, EMNLP.

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