PDF_Junior ML Research Engineer

Resistant AI is a fast-growing Series B tech scale-up helping financial institutions fight fraud and financial crime with AI through two avenues: document fraud detection and transaction monitoring. Currently, we are looking for a new Junior ML Research Engineer to join our team PDF Research team in Prague (the location is mandatory). What will you do? As a Junior ML Research Engineer, you will contribute to developing intelligent systems that detect fraud in PDF documents at scale. Your work will focus on identifying suspicious document behaviors, and deg machine learning solutions that detect anomalies and fraudulent modifications. This role combines research and production engineering. You will work end-to-end — from discovering new patterns in large datasets, deg experiments to measure their prevalence and correlations, to prototyping detection methods and integrating them into production systems. You will help build solutions that ensure trust, security, and integrity in document processing. Key Responsibilities PDF Research & Behavioral Analysis: Study the PDF format and document producer ecosystems, identify novel structural and behavioral patterns and design experiments to evaluate their correlation with fraudulent activity. Machine Learning & Anomaly Detection: Contribute to building, training, and evaluating machine learning models with a strong focus on fraud detection in document processing. From Prototype to Production: Translate research findings into clean, scalable production-ready code by integrating new detectors into the product pipeline. Optimization: Identify performance bottlenecks and find ways to optimize the current codebase through improved algorithms and selective rewrites of critical components in Rust. Team Work: Collaborate with researchers and engineers to experiment with new approaches that improve detection efficacy. What You Will Gain You will gain deep expertise in PDF document analysis and fraud detection while working