Research Scientist II

Who We Are 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. What you’ll do As a Research Scientist II on the Video team, you will independently drive core research initiatives, deliver reproducible experimental results, and help translate machine learning models into real-world product solutions. In this role, you will: 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. Who you are You remain focused, persistent, and scientifically rigorous through multiple experimental iterations, navigating ambiguity in research data, quality, or shifting methodologies while continuing to make steady progress. You work effectively across functional boundaries with research scientists, research engineers, and product partners, communicating research assumptions, findings, and implications with clarity. You take individual ownership of assigned research projects and workstreams, making sound technical decisions using available data, theory, and rigorous experimental evidence. You follow through on your commitments, communicate research progress transparently, and maintain the highest standard of technical integrity, documentation, and model reproducibility. You use machine learning frameworks effectively to design, train, and evaluate models, carefully validating outputs and applying sound judgment to balance experimentation speed with robustness, compliance, and model quality. Your skill-set Must-Haves: 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. Hands-on mastery of modern machine learning frameworks such as PyTorch, TensorFlow, or Keras. A proven track record of successful, timely project delivery and the ability to contribute to patents and peer-reviewed publications. Nice-to-Haves: Functional programming experience in C/C++ or working knowledge of deployment environments using Go, TF-Lite, and TF-Micro. Practical experience with real-time video processing pipelines and video data acquisition/preparation tasks. Foundational domain knowledge of biometrics, identity authentication, fraud patterns, or consumer secu