AIML Senior Machine Learning Research Engineer, LLM Post-training (Multilinguality)

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 whats possible in language detection at 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.

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 worlds 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 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 worlds linguistic diversity.

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 and maintain robust multilingual data pipelines for collection cleaning augmentation and curation of training data across diverse language comprehensive evaluation frameworks that go beyond standard benchmarks covering edge cases such as short text code-switching transliteration noisy input and near-identical language with product and engineering teams to integrate language identification capabilities into user-facing features iterating based on real-world performance and and apply techniques such as transfer learning multilingual pretraining and data augmentation to improve coverage for underrepresented lightweight model architectures and optimization strategies to enable efficient deployment across a range of device profiles from cloud to to best practices for model experimentation versioning and reproducibility within the team.

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

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