Reality Defender

The role focuses on building, tuning, and deploying state-of-the-art audio deepfake detection models for production environments. Responsibilities include investigating failure cases, implementing mitigation strategies for robustness, and collaborating with Product and Engineering teams.

Design, build, and optimize production-scale audio deepfake detection models while ensuring robustness against real-world noise and artifacts. Collaborate with clients and cross-functional teams to deploy scalable models and monitor performance using AI observability tools.

The Applied Scientist II will be responsible for building, tuning, and deploying state-of-the-art audio deepfake detection models, focusing primarily on ensuring their robustness, reliability, and performance in diverse, real-world client environments. This involves investigating failure cases, building custom evaluation frameworks, and driving model iteration to handle various real-world conditions like compression artifacts and noise.

The engineer will design, build, and optimize ML/DL models for production-scale audio deepfake detection while interfacing with clients to understand production environments and performance criteria. Responsibilities also include investigating failures, building custom evaluation frameworks, and driving model tuning for performance under real-world conditions.

The intern will conduct cutting-edge research focused on investigating and proposing new methods for detecting generative multi-modal content across audio and vision modalities. This involves performing research on multi-modal deepfake detection tasks, collaborating with the team, and writing up results for internal reports and academic submissions.