Sr. Applied ML Engineer, Apple Services Localization Engineering

Apple Apple · Big Tech · Seattle, WA +2 · Software and Services

This role focuses on designing, building, and shipping machine translation and LLM-based systems for Apple Services Localization. It involves taking models from prototype to production, owning serving, inference, and data pipelines, integrating ML models into existing systems, driving applied research (LLM fine-tuning, model compression, agentic workflows, RAG), and building evaluation infrastructure. The role requires strong software engineering skills and experience with deep learning toolkits, large models, and distributed production systems.

What you'd actually do

  1. 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.
  2. 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.
  3. Integrate ML models into Apple Services systems and APIs, partnering across teams to turn business objectives into robust technical solutions.
  4. Drive applied research and experimentation, including LLM fine-tuning, model compression, and emerging techniques such as agentic workflows and RAG.
  5. Build the evaluation infrastructure used to measure translation quality and system performance, and use it to guide iteration.

Skills

Required

  • Python
  • JAX
  • TensorFlow
  • PyTorch
  • Deep Learning
  • Large Language Models (LLMs)
  • Natural Language Processing (NLP)
  • machine translation
  • multilingual NLP
  • applied LLM research
  • MLOps
  • model-deployment infrastructure

Nice to have

  • PhD in a quantitative field
  • translation-quality evaluation
  • COMET
  • BLEU
  • human evaluation
  • quantization
  • distillation
  • compression
  • low-latency
  • high-throughput deployment
  • LLM-based agents
  • ReAct pattern
  • agentic workflows
  • RAG

What the JD emphasized

  • production-quality code
  • Proven track record training or deploying large models in production.
  • Experience building or operating large-scale, distributed production systems.

Other signals

  • shipping ML models
  • production systems
  • large-scale software development
  • massive scale
  • production-quality code
  • deploying large models in production
  • large-scale, distributed production systems