Job 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. 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. 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.

Requirements

Must-haves

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.
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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

accessibilitymachine learningmlpython

Platform: apple
Effort: medium