The Software Localisation team powers the tooling, features and processes used to create an exceptional localised experience for Apple’s international customers. We are responsible for localisation workflows, framework-level localisation support, localised OS features, and internal localisation tools used to create high quality experience across all of Apple’s software products around the world. We are looking for a creative, motivated, and passionate engineer who will work cross-functionally and globally to deliver the next generation of ML tooling and Apple Intelligence features for international locales. This person will be a creative, motivated, passionate technical leader who has a robust understanding of large language models (LLMs), natural language processing (NLP) and Apple software.
Description
As an Applied ML Engineer in the Software Localisation team, you will join an extraordinary team of passionate machine learning engineers to design, implement and qualify localisation features, processes and tooling across a wide variety of Apple’s products. We are looking for an exceptional candidate to help architect the future of localising ML models. Your role is to ensure Apple can deliver world-class software products to millions of users around the world including Apple Intelligence features like Writing Tools and Genmoji. Your duties include: - Leading the exploration and application of Large Language Models (LLMs) for performing data synthesis and translation tasks - Translating the latest research into high-quality systems and models that can be practically applied to further improve model localisation - Actively engaging in all aspects of model development, from ideation, training, experimentation, evaluation to deployment. - Collaborating with translation, localisation quality, project management and engineering teams to develop and implement solutions - Developing and maintaining features and tools to help facilitate the data and model localisation process
Minimum Qualifications
Preferred Qualifications
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Description
As an Applied ML Engineer in the Software Localisation team, you will join an extraordinary team of passionate machine learning engineers to design, implement and qualify localisation features, processes and tooling across a wide variety of Apple’s products. We are looking for an exceptional candidate to help architect the future of localising ML models. Your role is to ensure Apple can deliver world-class software products to millions of users around the world including Apple Intelligence features like Writing Tools and Genmoji. Your duties include: - Leading the exploration and application of Large Language Models (LLMs) for performing data synthesis and translation tasks - Translating the latest research into high-quality systems and models that can be practically applied to further improve model localisation - Actively engaging in all aspects of model development, from ideation, training, experimentation, evaluation to deployment. - Collaborating with translation, localisation quality, project management and engineering teams to develop and implement solutions - Developing and maintaining features and tools to help facilitate the data and model localisation process
Minimum Qualifications
- Experience with Machine Learning (ML), with a particular emphasis on Large Language Models (LLMs) and Natural Language Processing (NLP)
- Strong programming skills in Python, Swift, C or C++ or other language
Preferred Qualifications
- BS, MS or PhD in Computer Science, Computational Linguistics, Artificial Intelligence, or Machine Learning
- 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)
- Experience in software localisation / internationalisation Native-level foreign language skills in reading, writing and speaking
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