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Software Engineer - Applied Machine Learning & Localization

Apple

$147,400 - $272,100
Aug 18, 2025
Cupertino, CA, US
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Apple is looking for an engineer to help architect the future of localizing models and machine translation to ensure Apple and third-party developers using Xcode can deliver world-class software products to millions of users around the world.

Requirements

  • Experience with Machine Learning (ML), with a particular emphasis on Large Language Models (LLMs) and Natural Language Processing (NLP)
  • Proven ability to comprehend, interpret, and apply cutting-edge research into consumer-oriented products
  • Comprehensive knowledge and hands-on experience with fine-tuning approaches and training models
  • Experience adapting pre-trained LLMs for downstream tasks
  • Proficiency using open-source ML toolkits and frameworks (e.g. PyTorch, TensorFlow)
  • Strong programming skills in Python, Swift, C or C++
  • Familiarity with localization standards and technologies (Unicode, XLIFF, ICU, etc.)

Responsibilities

  • Leading the exploration and application of Large Language Models (LLMs) for performing translation tasks and Apple product features
  • Translating the latest research into high-quality systems and models that can be practically applied to further improve machine translation or Apple product features
  • Actively engaging in all aspects of model development, from ideation, training, experimentation to deployment.
  • Collaborating with translation, localization quality, project management and engineering teams to develop and implement solutions
  • Developing and maintaining features, frameworks and tools to help facilitate the software translation process

Other

  • BS, MS or PhD in Computer Science, Artificial Intelligence, or Machine Learning (or equivalent experience)
  • Experience in software localization / internationalization
  • Native-level foreign language skills in reading, writing and speaking
  • Comfortable working cross-functionally and cross-culturally
  • Ability to work in a fast-paced environment with frequent context switching and changing priorities