About the job
About Us
Reality Defender is an award-winning cybersecurity firm dedicated to assisting enterprises and government agencies in the detection of deepfakes and AI-generated media. We leverage a patented multi-model methodology that stands resilient against the forefront of generative platforms producing video, audio, images, and text. Our API-first deepfake detection platform empowers teams and developers to uncover fraud, disinformation campaigns, and harmful deepfakes in real time.
Supported by prominent investors such as DCVC, Illuminate Financial, Y Combinator, Booz Allen Hamilton, IBM, Accenture, Rackhouse, and Argon VC, Reality Defender collaborates with esteemed enterprise clients, financial institutions, and governmental organizations to ensure that AI-generated media is not exploited for malicious intents.
YouTube: Reality Defender Wins RSA Most Innovative Startup
The Role: Applied Scientist II - Audio
We are on the lookout for an Applied Scientist II to develop, fine-tune, and deploy cutting-edge audio deepfake detection models in real-world client settings.
Your primary responsibilities will include model tuning and deployment, ensuring robustness, reliability, and performance under a variety of real-world testing conditions, addressing adversarial and edge-case scenarios. This position demands extensive hands-on experience in model building, training, and benchmarking.
Responsibilities
- Tune and optimize ML/DL models for scalable audio deepfake detection.
- Analyze failure scenarios in client environments, build custom evaluation frameworks, and implement strategies to enhance model robustness.
- Guide model iterations to ensure performance across various real-world conditions, such as compression artifacts, noise, telephony, and streaming pipelines.
- Communicate technical findings and model performance insights to internal stakeholders.
- Collaborate with Product and Engineering teams to gain a comprehensive understanding of the production environment and incorporate relevant evaluations for performance assessment.
Candidate Profile
- Master’s or PhD in Computer Science, Machine Learning, Signal Processing, or a related field.
- Recent PhD graduate or a Master's degree holder with 3+ years of industry experience in building and deploying ML/DL models for AI applications.
- Strong foundation in machine learning, deep learning, and audio signal processing.
