Research Scientist II – Deepfake Detection & Authentication (Video & Audio)

Who We Are

Pindrop is a cybersecurity company delivering continuous identity verification across voice, video, and digital interactions. Powered by the Pindrop Intelligence Network, our unified trust platform helps enterprises answer three critical questions:

Is this identity synthetic? Detect AI-generated and manipulated voice and video. Is this identity risky? Identify fraud, suspicious behavior, and other risk signals. Is this identity known? Authenticate identities using voice, device, behavioral, and other signals.

Pindrop brings this intelligence together across purpose-built trust engines to help organizations make informed decisions in real time — protecting customers, workforce interactions, and emerging AI-driven interactions.

Pindrop protects billions of high-risk interactions for enterprises, including leading financial institutions, insurers, and healthcare organizations. Its technology is informed by more than 8 billion real-world interactions annually and protected by 300+ patents.

Recognized by TIME as one of the 10 Most Influential Software Companies of 2026 and by Inc. for Best in Business for Innovation, Pindrop is backed by leading investors including Andreessen Horowitz, IVP, and CapitalG.

What you’ll do

As a Research Scientist II on our core Research team, you will independently drive research initiatives, deliver reproducible experimental results, and help translate machine learning models into real-world product solutions across deepfake video, deepfake audio, and voice/identity authentication. In this role, you will:

• Conduct research to develop new functionalities and improve existing technologies for synthetic media detection, multimodal deepfake identification (audio and video), and real-time identity authentication. • Design, implement, and evaluate deep learning algorithms related to audio-visual deepfake detection, speech synthesis detection, facial manipulation analysis, and biometric authentication. • Design, develop, and maintain internal research packages and pipelines to train, evaluate, and benchmark ML models, ensuring computational efficiency and absolute reproducibility. • Partner closely with research engineers and engineering teams to transition complex research prototypes into efficient, scalable, production-ready systems across cloud and edge platforms. • Support targeted investigations by analyzing model behavior, edge cases, attack vectors, and detection gaps, turning insights into measurable improvements in model robustness. • Participate in regular code and research reviews, cross-functional project syncs, and contribute to patents and peer-reviewed papers for top-tier computer vision, speech, audio, and machine learning conferences.