Machine Learning Engineer - Text Language Identification

Summary

The Multilingual Intelligence team is looking for a machine learning engineer to build the next generation of text language identification systems. You will develop models that accurately detect and classify languages across diverse scripts, regions, and user contexts — forming the backbone of multilingual features used by millions of people worldwide. You will work alongside a team of world-class experts to explore novel modeling approaches, data strategies, and evaluation methodologies that push the boundaries of what's possible in language detection at scale. Passionate about Natural Language Processing, multilingual systems, and building ML that works for everyone regardless of the language they speak? Join us to make every device fluent in every language.

Minimum Qualifications

2+ year experience in machine learning, NLP, or related fields, with hands-on experience training and evaluating neural models. Proficient programming skills in Python and at least one major deep learning framework such as PyTorch, TensorFlow, or JAX. Bachelor's or Master's degree, or equivalent practical experience, in Computer Science, Machine Learning, Computational Linguistics, or a related technical field. Experience working with multilingual, multi-script, or cross-lingual datasets.

Preferred Qualifications

Experience with text classification, language identification, dialect identification, or similar NLP tasks involving multiple languages. Familiarity with embedding models, sentence representations, or contrastive learning methods. Understanding of model optimization techniques such as quantization, pruning, or knowledge distillation, and a general interest in efficient inference. Experience with low-resource languages, code-switching, or mixed-language input handling. Familiarity with Hugging Face ecosystem (transformers, tokenizers, datasets) and modern NLP pipelines. Ability to formulate a problem, design experiments, and implement end-to-end solutions in Python and Bash. Strong communication skills and a passion for working cross-functionally across Research, Engineering, and Product teams.

Description

Text language identification is a foundational capability that powers multilingual experiences across products — from translation and search to content recommendation and accessibility. As the number of supported languages grows and user expectations rise, the challenge is building models that are not only accurate but also fair, robust, and efficient across the full spectrum of the world's languages. We are looking for a machine learning engineer passionate about building high-quality, inclusive language technology. As a member of the team, you will work across the full model lifecycle — from data curation and model design to evaluation and deployment. You will collaborate with researchers, engineers, and linguists to ensure our language identification systems perform reliably for users everywhere, regardless of how they write or what language they use. The successful candidate should be a strong team player with excellent oral and written communication skills and a genuine passion for building ML systems that work for the world's linguistic diversity.

Responsibilities

Design, train, and evaluate text language identification models that achieve high accuracy across hundreds of languages, scripts, and locales — including low-resource and mixed-language scenarios. Build and maintain robust multilingual data pipelines for collection, cleaning, augmentation, and curation of training data across diverse language sources. Develop comprehensive evaluation frameworks that go beyond standard benchmarks, covering edge cases such as short text, code-switching, transliteration, noisy input, and near-identical language pairs. Collaborate with product and engineering teams to integrate language identification capabilities into user-facing features, iterating based on real-world performance and feedback. Research and apply techniques such as transfer learning, multilingual pretraining, and data augmentation to improve coverage for underrepresented languages. Explore lightweight model architectures and optimization strategies to enable efficient deployment across a range of device profiles — from cloud to edge. Contribute to best practices for model experimentation, versioning, and reproducibility within the team.