Sr. Applied ML Engineer, Apple Services Localization Engineering

Are you passionate about building extraordinary products and embracing state-of-the-art techniques and technologies? In Apple Services Localization Engineering, we are leveraging Machine Learning and Machine Translation to expand our global product reach and bring our Services to more users around the world. The Apple Services Engineering team is one of the most exciting examples of Apples passion for combining art and technology. These are the people who power Apple Music, the App Store, Apple TV, Apple Fitness+, Apple Podcasts, Apple Books, and more. And they do it on a massive scale, meeting Apples high expectations with high performance to deliver a huge variety of entertainment in over 37 languages to more than 175 countries. These engineers build secure, end-to-end solutions. They 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.

Thanks to Apples outstanding integration of hardware, software, and services, engineers here partner to get behind a unified vision. That vision always includes a deep dedication to strengthening Apples privacy policy, one of Apples core values. Although Services are a bigger part of Apples business than ever before, these teams remain nimble and cross-functional, offering greater exposure to the array of opportunities here. 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.

Does this sound like you? Join our team!In This Role, You Will:

Design, build, and ship machine translation and LLM-based systems that power Localization across Apple Services, including Apple Music, the App Store, subscription services, and marketing campaigns. Take models from prototype to production: build and own the serving, inference, and data pipelines that run reliably at massive scale, with a focus on latency, throughput, and cost. Integrate ML models into Apple Services systems and APIs, partnering across teams to turn business objectives into robust technical solutions. Drive applied research and experimentation, including LLM fine-tuning, model compression, and emerging techniques such as agentic workflows and RAG. Build the evaluation infrastructure used to measure translation quality and system performance, and use it to guide iteration. Communicate results, recommendations, and trade-offs clearly to both technical and non-technical audiences, including leadership and operations partners. Mentor engineers and foster a culture of collaboration, technical excellence, and innovation. Where it aligns with Apples innovation standards, contribute to publications and patents. BS/MS/PhD in a quantitative field (Computer Science, Math, Statistics, Physics, etc.) and 5+ years of relevant experience. Strong software engineering fundamentals and proficient programming skills in Python, with experience writing and maintaining production-quality code. Hands-on experience with deep learning toolkits such as JAX, TensorFlow, or PyTorch. Proven track record training or deploying large models in production. Experience building or operating large-scale, distributed production systems. Deep understanding of Deep Learning, Large Language Models (LLMs), and Natural Language Processing (NLP).PhD in a quantitative field, or equivalent depth in machine translation, multilingual NLP, or applied LLM research. Experience with machine translation and translation-quality evaluation (e.g., COMET, BLEU, human evaluation). Experience optimizing model serving and inference - quantization, distillation, or compression - for low-latency, high-throughput deployment. Familiarity with MLOps and model-deployment infrastructure. Experience building LLM-based agents, including the ReAct pattern in agentic workflows.