Machine Learning Architect , LLM u0026 Generative AI

The 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 Ru0026D 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 u0026 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 Ru0026D 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. Collaboration and Mentorship:
Partner with rest of Apple teams to transition technology breakthroughs into production grade solutions. Guide and mentor machine learning engineers and researchers to foster technical excellence .

Experience in multi-modal models (e.g., image, video, audio, or motion modalities) Familiarity with emerging technologies such as Mixture of Experts, LoRA, and Retrieval Augmented Generation Strong academic track record with publications in top tier conferences (NeurIPS, CVPR, ICLR, etc)

Masters, or Ph.D. in Computer Science, or Computer Engineering; similarly related fields, or comparable professional experience Proficiency in toolkits like PyTorch or other deep learning frameworks 15+ years in machine learning, with at least 2 years of experience in LLMs, diffusion models, or other generative image/video models Experience in distributed training, model parallelism, and deployment of large-scale generative models Knowledge of techniques such as quantization, distillation, and efficient inference.

Experience with deploying large ML models in real world products Strong background in conducting experiments, analyzing results, and iterating on model improvements.