NLP Engineer

• Formalise linguistic phenomena into rules, taxonomies, annotation criteria, and system or model requirements
• Design, evaluate, and improve prompts and AI components for language-learning use cases
• Build datasets for training, calibration, and evaluation, including annotation guidelines and quality-control processes
• Evaluate model behaviour using qualitative and quantitative methods
• Investigate failure modes and quality issues and validate fixes across prompts, models, data, and downstream processing
• Use Python and existing tooling to process linguistic data and automate evaluation
• Collaborate with engineers, product managers, learning designers, and computational linguists
• Develop reusable solutions, resources, and practices that scale across use cases and languages
• Stay informed about advances and best practices in NLP and LLM research

Explore Senior Computational Linguist at Preply. Review requirements and application details.