This role pays less than
52%
of similar roles. Most pay
$187,762–$254,750
— the shaded band above.
At the midpoint, this role pays about
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versus about
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Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
2000 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1613 roles with salary data.
As part of the Multilingual Intelligence team, the AI/ML - Machine Learning Engineer, Text Language Identification will build next-generation systems to detect and classify languages across diverse scripts, regions, and user contexts. This role involves managing the full model lifecycle, including data curation, training, evaluation, and deployment. You will develop robust multilingual data pipelines, design models for low-resource and mixed-language scenarios, and create comprehensive evaluation frameworks for edge cases like code-switching and transliteration. The position requires proficiency in Python, Bash, and deep learning frameworks such as PyTorch, TensorFlow, or JAX. Candidates should have experience with neural models, the Hugging Face ecosystem, and techniques like transfer learning or quantization. This work addresses the core challenge of creating accurate, fair, and efficient language identification to power features like translation, search, and content recommendation.
What does a Machine Learning Engineer earn in California?
Median $246150 from 178 postings across 30 companies.
Design, train, and evaluate text language identification models across hundreds of languages and scripts.
Build and maintain robust multilingual data pipelines for collection, cleaning, and augmentation.
Develop comprehensive evaluation frameworks covering edge cases like code-switching and noisy input.
Research and apply transfer learning and multilingual pretraining to improve coverage for underrepresented languages.
Optimize model architectures for efficient deployment across various device profiles from cloud to edge.
Integrate language identification capabilities into user-facing features based on real-world performance feedback.
Establish best practices for model experimentation, versioning, and reproducibility within the team.
What we're looking for
2+ years of experience in machine learning, NLP, or related fields with hands-on experience training and evaluating neural models.
Proficiency 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 (preferred).
Familiarity with embedding models, sentence representations, or contrastive learning methods (preferred).
Understanding of model optimization techniques such as quantization, pruning, or knowledge distillation (preferred).
Familiarity with the Hugging Face ecosystem and modern NLP pipelines (preferred).
Python
PyTorch
Spark
Airflow
Computer Vision
Natural Language Processing
GANs
ETL
ELT
Distributed Training
Data Augmentation
Machine Learning
Deep Learning
Data Observability