The Localization Automation team builds the tools and technologies that enable Apple to localize and deliver products such as iPhone, iPad, Apple Watch, Mac, Vision Pro, and more to customers around the world. We’re looking for a passionate and driven Software Engineer to help evolve Apple’s localization systems and processes at scale.
In this role, you’ll work on critical services and workflows that touch nearly every Apple product. You’ll design and build new tools, improve existing platforms, and help ensure Apple’s experiences feel native, natural, and world-class in every market.
Description
You’ll thrive in a fast-paced, highly collaborative environment where quality, precision, and scalability matter. This role requires a strong technical foundation, excellent problem-solving skills, and a genuine interest in building robust systems that support global software delivery.
You’ll collaborate closely with engineers across Apple to support new features, improve automation, and solve complex integration challenges. Attention to detail, strong ownership, and the ability to balance multiple priorities are key to success.
","responsibilities":"Design, develop, and maintain scalable back-end web services
Improve and evolve localization automation tools and workflows
Partner cross-functionally with engineering teams to support new software features
Troubleshoot and resolve complex system and integration issues
Contribute to architecture decisions and long-term technical strategy
Preferred Qualifications
Experience using generative AI tools to support software development
Proficiency in Python and/or Ruby
Native-level proficiency in one or more foreign languages
A proven track record of shipping high-quality products at scale
Minimum Qualifications
Bachelor’s or Master’s degree in Computer Science, or equivalent practical experience
Experience with back-end frameworks such as Ruby on Rails or Django
Interest in localization, internationalisation, translation workflows, and global markets
Experience with Machine Learning, with an emphasis on Large Language Models (LLMs)
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