Engineering Manager, Machine Learning & Nlp, Input Experience

Apple Apple · Big Tech · Cupertino, CA +1 · Machine Learning and AI

Engineering Manager for Machine Learning & NLP within the Input Experience team at Apple, focusing on enhancing user experience with language understanding and personalized text composition. The role involves building the future of Generative AI features like Writing Tools, Summarization, and Smart Actions, and evolving data, tooling, modeling, and evaluation pipelines with agentic harnesses for global scale. The team is shifting the ML product development paradigm to agentic workflows, covering feature definition, data synthesis, model training, auto-evaluation, and user feedback.

What you'd actually do

  1. enable the next-generation of agentic ML product development systems at scale using Apple Foundation Models.
  2. explore, design, and implement emerging techniques, ensuring alignment with product goals, privacy requirements, and performance metrics in a hands-on role.
  3. redefine the ML development process across features and languages: problem formulation, experimentation, evaluation, fine-tuning, and continuous improvement that expand both the depth of Apple Intelligence’s capabilities and the breadth of its support for our global customer base.
  4. building and refining the personalized agent trajectory pipelines for data and evaluation across synthetic personas and languages.

Skills

Required

  • Masters or PhD in Computer Science, Electrical Engineering, Physics, Statistics or related field; or equivalent practical experience
  • Prior experience with technical leadership or engineering management
  • Strong foundation in Data Science and MLOps
  • Familiarity with product ML/NLP lifecycle
  • Familiarity with techniques such as SFT, RLHF, Data Synthesis, Parameter-Efficient Fine-Tuning, LLM-judge evaluation
  • Excellent communication skills

Nice to have

  • Experience with deploying large ML models for real world products and leading high-performing teams
  • Experience curating, filtering, and synthesizing high-quality training datasets at scale
  • Familiarity with ML pipelines that need to scale across languages
  • Experience developing and training models for agentic workflows, tool calling and advanced reasoning techniques
  • Familiarity with working and managing complex, large-scale codebases, with a strong emphasis on writing high-quality, maintainable, and well-tested code.
  • Experience using AI-assisted development tools (e.g., Claude, Copilot, or similar) to accelerate experimentation, code development, and research workflows

What the JD emphasized

  • agentic harnesses
  • agentic workflows
  • data synthesis
  • model training
  • auto-evaluation
  • model probing
  • evaluation
  • user feedback
  • agentic ML product development systems
  • agentic workflows
  • agentic workflows
  • agentic workflows
  • agentic workflows

Other signals

  • Apple Intelligence
  • Generative AI
  • foundation models
  • agentic workflows
  • ML product development