Security Research

We are looking for a hands-on security researcher who combines an attacker mindset, fraud-domain curiosity, strong technical depth, and data analysis skills. You will investigate real-world fraud patterns, reproduce attacker techniques, analyze telemetry, enrich our data collection capabilities, discover new security signals, and work closely with product, data, and engineering teams to turn research insights into production-grade detection and prevention mechanisms.This role is especially relevant for researchers with experience in browser technologies, mobile-native applications, or both. You may work on desktop and mobile browsers, browser APIs, browser automation, client-side signals, native mobile applications, Android/iOS behavior, mobile security, emulators, device signals, and mobile automation.You will help expand our visibility into new signals and patterns that improve fraud detection, model performance, and customer protection.If you are a talented researcher who enjoys solving complex problems, working with messy real-world data, and building practical defenses against modern fraud and abuse, we want you to join our team.Requirements: At least 3 years of experience in security research, fraud research, detection engineering, threat research, mobile security research, browser security research, or a similar hands-on technical role.Bachelors degree in Computer Science, Cybersecurity, Data Science, or a related field or equivalent hands-on experience.Strong hands-on experience with at least one of the following areas:Desktop or mobile browsers, browser APIs, browser automation, browser fingerprinting, web signals, or client-side web security.Native mobile applications for Android or iOS, mobile OS behavior, device signals, emulators, mobile automation, app instrumentation, or mobile security.Strong understanding of web technologies, mobile technologies, APIs, application behavior, and modern attack techniques.Strong Python skills and experience building research tools, automation, data analysis workflows, detection prototypes, or feature engineering pipelines.Experience analyzing messy real-world data, investigating anomalies, validating hypotheses, and drawing practical conclusions from incomplete information.Familiarity with machine learning training and validation concepts, such as train/test split, validation sets, overfitting, leakage, feature quality, precision/recall, false positives, false negatives, and model evaluation.Ability to produce data features in a structured, reliable, and model-friendly way.Ability to think like an attacker while designing reliable, scalable, and explainable defenses.Strong problem-solving skills, independence, persistence, and a getting things done attitude.Excellent communication and interpersonal skills.Ability to work closely with engineering, product, and data science teams and translate research insights into practical product capabilities.This position is open to all candidates.