Machine Learning Architect, SIML - LLM & Generative AI
Apple · Seattle, WA · Software and Services
About this role
Apple is hiring a senior-level ML Platform Engineer in the machine learning function based in Seattle, WA. The posting calls out experience with PyTorch, LLMs, RAG, Deep Learning.
- Role
- ML Platform Engineer
- Function
- machine learning
- Level
- senior
- Track
- Individual contributor
- Location
- Seattle, WA
- Department
- Software and Services
- Posted
- Feb 27, 2026
More roles at Apple
Job description
from Apple careersThe System Intelligence and Machine Learning (SIML) organization at Apple is looking for an experienced and visionary Machine Learning Architect to drive technology direction, shape our machine learning strategy, and lead pioneering R&D efforts. In this role, you will define and guide the development of technologies focusing on improving both the quality and performance of advanced large language models (LLMs) and generative AI models for image and video generation.
You will work closely with cross-functional teams, including researchers, engineers, and product leaders, to deliver cutting-edge AI solutions that push the boundaries of generative technologies both on cloud and on edge devices that reach billions of users.
In this ML architect role, the key responsibilities include:
Technology Strategy & Direction: Define the technical roadmap for improving the quality and performance in LLMs and generative models, ensuring alignment with business objectives.
Technology and Industry Leadership: Lead R&D initiatives in areas such as large-scale model optimization, hardware and software co-design, diffusion models, multi-modal AI, and generative video synthesis. Stay up-to-date with advancements in Generative AI to incorporate emerging technologies into our solutions.
Architecture Design: Develop scalable, efficient architectures for training, optimizing, and deploying large-scale LLMs and generative models.
Innovation and Experimentation: Explore and prototype novel techniques in generative AI, including fine-tuning, reinforcement learning with various of reward strategies, transfer learning, and multimodal alignment.