The Multilingual Intelligence team is seeking a machine learning engineer to develop advanced text language identification systems. This role involves creating models that accurately detect and classify languages across various scripts and contexts, contributing to multilingual features used globally. The engineer will collaborate with experts in the field to explore innovative modeling approaches and data strategies. A passion for Natural Language Processing and building inclusive language technology is essential.

• 2+ years of 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.
• Experience with low-resource languages, code-switching, or mixed-language input handling.
• Familiarity with the Hugging Face ecosystem (transformers, tokenizers, datasets) and modern NLP pipelines.
• Strong communication skills and a passion for working cross-functionally across Research, Engineering, and Product teams.

• Develop models for text language identification that are accurate and efficient.
• Collaborate with researchers, engineers, and linguists to enhance language identification systems.
• Work across the full model lifecycle, including data curation, model design, evaluation, and deployment.
• Explore novel modeling approaches and data strategies to improve language detection capabilities.
• Ensure that language identification systems perform reliably for users across diverse languages and scripts.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.