Senior Research Engineer - Learning to Rank

In our domain, we focus on delivering the core search and retrieval capabilities for the Allegro AI Assistant. We maintain a critical entry point for users looking to discover products, compare offers, and get personalized recommendations through natural conversations. We design, implement, and operate an extensive system combining modern microservice architecture with Large Language Models, high-performance retrievers, and vast data sources. We care about data quality, robust context building, and near real-time accuracy to best guide users through a highly dynamic marketplace via an intelligent, conversational interface.

• Master's or PhD in machine learning, mathematics, computer science, statistics or other STEM fields
• Good knowledge of deep learning techniques (neural networks, contrastive learning, semi-supervised learning) in at least one domain (information retrieval, natural language generation or understanding, etc.)
• Knowledge of scientific research methodology and iterative experimental processes
• Experience working with real data that deviate from standard research collections
• Proficiency in Python and libraries for model development (PyTorch, Tensorflow, Transformers, Pandas, Numpy, etc.)

Nice to have:

• Understanding of Information Retrieval (IR) area
• Published research papers in ML-related topics
• Experience in business cooperation and deployments of ML-based solutions in commercial environments
• Experience running large-scale computation on cloud platforms (GCP, AWS, or Azure)
• Experience using LLMs for synthetic data generation or solving business problems

• Design and deliver production-ready machine learning solutions for Allegro products
• Analyze and understand complex data sets, identifying relevant data sources for ML use cases
• Train, evaluate, and experiment with applied ML models using reliable evaluation methods
• Translate state-of-the-art ML research into practical improvements for real-world solutions
• Select ML tools and techniques that best fit concrete business needs
• Work closely with cross-functional teams to deploy ML solutions into production
• Spread ML expertise through internal sessions, presentations, and research activities
• Support junior team members and act as a trusted ML expert within the organization