Applied Scientist Intern (Audio) - Reality Defender : Job Details

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.

• 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.

• 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 language models, speech foundation models, and 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.