Sr. Applied ML Engineer, Apple Services Localization Engineering

About the role

We build and develop the core language and machine translation models that power Localization across Services in an efficient and scalable manner — and the production systems that put those models in front of users. We work on a wide spectrum of approaches, including agentic workflows, foundation modeling, deep learning, model compression, and transfer learning. We also build the systems that power Apple Music lyrics translations and lyrics transliterations (phonetic pronunciation). This position spans applied modeling and the software engineering needed to ship at scale, offering a unique chance to work where Localization meets state-of-the-art, large-scale software development.

About Apple Services Engineering

The Apple Services Engineering team powers Apple Music, the App Store, Apple TV, Apple Fitness+, Apple Podcasts, Apple Books, and more. These engineers build secure, end-to-end solutions and develop the custom software used to process all the creative work, the tools that providers use to deliver that media, all the server-side systems, and the APIs for many Apple services. Services are delivered in over 37 languages to more than 175 countries. Engineers here partner to get behind a unified vision that includes a deep dedication to strengthening Apple's privacy policy, one of Apple's core values. Although Services are a bigger part of Apple's business than ever before, these teams remain nimble and cross-functional, offering greater exposure to the array of opportunities available.

Minimum qualifications

• Education: BS/MS/PhD in a quantitative field (Computer Science, Math, Statistics, Physics, etc.)
• Experience: 5+ years of relevant experience
• Software engineering: Strong software engineering fundamentals and proficient programming skills in Python, with experience writing and maintaining production-quality code
• Deep learning: Hands-on experience with deep learning toolkits such as JAX, TensorFlow, or PyTorch
• Large models: Proven track record training or deploying large models in production
• Distributed systems: Experience building or operating large-scale, distributed production systems
• Expertise: Deep understanding of Deep Learning, Large Language Models (LLMs), and Natural Language Processing (NLP)

Preferred qualifications

• Education: PhD in a quantitative field, or equivalent depth in machine translation, multilingual NLP, or applied LLM research
• Machine translation: Experience with machine translation and translation-quality evaluation (e.g., COMET, BLEU, human evaluation)
• Model optimization: Experience optimizing model serving and inference — quantization, distillation, or compression — for low-latency, high-throughput deployment
• MLOps: Familiarity with MLOps and model-deployment infrastructure
• LLM agents: Experience building LLM-based agents, including the ReAct pattern in agentic workflows