Junior ML Research Engineer

Role Overview

As a Junior Machine Learning Research Engineer, you will contribute to developing cutting-edge technologies that safeguard businesses and individuals from fraud in the new era of large language models. Your work will center on designing and implementing intelligent systems capable of detecting subtle modifications and fraudulent activities in digital documents. You will contribute to the full cycle of machine learning model development from research and design through implementation, evaluation, and deployment, applying advanced machine learning and deep learning techniques. You will help build solutions that ensure trust, security, and integrity in document processing. 

Key Responsibilities

• Algorithm Development & Research: Assist in the design and development of algorithms aimed at detecting modifications and anomalies in digital documents.

• Machine Learning Model Engineering: Contribute to building, training, and evaluating machine learning models with a strong focus on fraud detection in document processing.

• Deep Learning & Tools: Apply state-of-the-art frameworks such as PyTorch to develop and deploy scalable fraud detection models.

• Data Management & Analysis: Work with large datasets of documents containing various fraud scenarios. Use preprocessing techniques such as cleaning, feature engineering, and augmentation to expose anomalies and strengthen model accuracy.

• Fraud-Focused Research: Research and experiment with innovative methods to improve detection rates and minimize false positives, ensuring models are both robust and trustworthy.

What You Will Gain

This role provides an opportunity to grow and deepen your expertise in applied machine learning while directly impacting how organizations prevent fraud in the digital world. You will gain hands-on experience with advanced fraud detection systems, and work with state-of-the-art technologies in a collaborative and supportive environment.

Requirements

• Strong foundation in machine learning concepts, with exposure to deep learning architectures.

• Good communication and teamwork abilities in a collaborative environment.

• At least 1 year hands-on experience with Python and familiarity with ML frameworks such as PyTorch. 

• Basic knowledge of data preprocessing techniques (cleaning, feature extraction, augmentation) and their impact on model performance.

• Familiarity with version control tools such as Git and collaborative coding practices.

• Optional experience with cloud technologies (AWS, GCP, Azure)