Research Scientist II
Pindrop is the Real Human + Right Human Identity Trust Platform for the AI era. As AI-driven fraud and deepfakes erode trust in digital communication, Pindrop delivers continuous identity verification and deepfake detection across voice, video, and digital interactions in real time. Enterprises rely on Pindrop to secure billions of high-risk customer interactions each year, including top U.S. banks, as well as leading insurers and healthcare providers. Powered by models trained on more than 1.5 billion real-world interactions annually and protected by 300+ patents, Pindrop restores trust while reducing fraud, lowering operational costs, and improving customer experience. Recognized by TIME as one of the Top 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.
Responsibilities
- Conduct research to develop new functionalities and improve existing technologies for real-time video processing and meeting analytics.
- Design, implement, and evaluate deep learning algorithms related to video processing, face recognition, and video deepfake detection.
- Design, develop, and maintain internal research packages to train and test ML models, ensuring absolute reproducibility and computational efficiency.
- Partner closely with research engineers and engineering teams to help deploy research models and tools into production environments.
- Participate in cross-functional team meetings, contribute to regular research and code reviews, and publish patents and peer-reviewed papers in top computer vision, audio, and speech conferences.
Qualifications
- PhD in a quantitative field (e.g., Computer Science, Mathematics, Engineering, Artificial Intelligence) or equivalent industry research experience.
- 3+ years of professional experience in Deep Learning, specifically applied to computer vision, face recognition, video deepfake detection, or general machine learning.
- Strong programming proficiency in Python with a proven ability to design, develop, and maintain research packages to train and test ML models.