Senior ML Engineer — Multilingual Text Identification

Apple Inc.; Cupertino (CA); Hybrid; USD 150,000 - 278,000; Full time

Job summary

Apple Inc. in Cupertino, CA is seeking a Machine Learning Engineer to advance text language identification across diverse languages and scripts. You will build robust models, curate multilingual data, and develop evaluation frameworks to ensure fairness and robustness at scale.

The role emphasizes collaboration with research, engineering, and product teams, applying transfer learning and efficient inference to deploy across devices and cloud environments.

Qualifications

• 2+ years in ML/NLP with neural models.
• Python and at least one major DL framework (PyTorch, TensorFlow, or JAX).
• Bachelor's or Master's in CS/ML/Computational Linguistics or related field.
• Experience with multilingual datasets.

Responsibilities

• Design, train, and evaluate text language identification models across hundreds of languages.
• Build multilingual data pipelines for collection, cleaning, augmentation, and curation of training data.
• Develop evaluation frameworks covering short text, code-switching, transliteration, and noisy input.
• Collaborate with product and engineering teams to integrate capabilities into features.
• Research transfer learning, multilingual pretraining, and data augmentation for underrepresented languages.
• Explore lightweight architectures and optimization for edge-to-cloud deployment.
• Contribute to best practices for model experimentation and reproducibility.

Skills

Python

NLP

Multilingual data handling

PyTorch

TensorFlow

JAX

Communication

Education

Bachelor's or Master's in CS/ML/Computational Linguistics

Tools

Hugging Face

Transformers

Tokenizers

Datasets

Job description

Apple Inc. in Cupertino, CA is seeking a Machine Learning Engineer to advance text language identification across diverse languages and scripts. You will build robust models, curate multilingual data, and develop evaluation frameworks to ensure fairness and robustness at scale.

The role emphasizes collaboration with research, engineering, and product teams, applying transfer learning and efficient inference to deploy across devices and cloud environments.