Senior Research Scientist

Join our team as a Senior Research Scientist, where you'll tackle the critical challenge of online identity verification and fraud detection. Your research will focus on detecting AI-generated and manipulated images, understanding model performance across populations, and uncovering patterns in attacker behavior. You'll collaborate with our ML and engineering teams to turn your findings into real-world defenses and have the opportunity to publish your work in top venues.

• Lead research on open problems in identity verification and fraud prevention, using focused experiments to test new ideas quickly.
• Develop methods to detect manipulated documents, AI-generated images and emerging fraud attacks.
• Partner with ML and engineering to turn research prototypes into production models.

• A demonstrated record of peer-reviewed publications in relevant conferences or journals, with substantive contributions to the research
• Deep expertise in one of the following:
• Computer vision: research in image forensics, face analysis, generative models or representation learning
• A PhD in Computer Science, Machine Learning, Mathematics, Statistics, or a related technical field is strongly preferred. We will also consider candidates with an MS and an exceptional research record
• Independently lead research, design rigorous experiments, and use results including negative or unexpected findings to guide next steps
• Identity or fraud prevention: Experience researching identity verification, biometrics, fraud detection, out-of-distribution detection, adversarial behavior, or closely related security problems
• Mathematics and statistics: strong foundation in areas such as probability, statistical inference, optimization, or applied mathematics, with experience applying it to empirical research
• Clear communication, including the ability to explain experimental design, findings, and limitations to technical and non-technical audiences
• Research on deepfakes, presentation attacks or adversarial robustness
• Experience shipping ML research to production
• Experience with noisy, imbalanced real-world data