Algorithm Engineer Intern - Monee Credit (May to Aug 2026)

What you'll do

• Conduct research on cutting-edge sequence modeling and representation learning approaches, including Transformer-based architectures and transaction foundation models
• Read, reproduce, and implement recent papers related to user behavior modeling, sequential recommendation, NLP-inspired pretraining, and large-scale representation learning
• Build and optimize transaction sequence modeling pipelines using large-scale user behavioral and credit-related datasets
• Explore improvements in model architecture, training objectives, and feature representations to enhance downstream risk prediction performance
• Collaborate with risk modeling, data engineering, and strategy teams to evaluate and productionize research findings into real-world risk applications

Requirements

• Undergraduates from a degree in Computer Science, Business Analytics and related fields
• Full-time interns preferred (3 to 6 months)
• Past internship experience in sequence modelling or NLP related model training is compulsory
• Strong programming skills in Python; familiarity with SQL and Spark/Hadoop is a plus
• Good understanding of machine learning and deep learning fundamentals, especially sequence modeling and Transformer architectures
• Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow
• Strong interest in large-scale AI models, representation learning, NLP-inspired modeling, or recommendation systems
• Ability to read and understand research papers and reproduce experimental results independently
• Strong analytical thinking and curiosity toward applying advanced AI techniques to real-world risk problems