Apple
Machine Learning Architect LLM & Generative AI (ImageVideo)
Apple, Seattle, Washington, us, 98127
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 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 .
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.
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)
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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.
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)
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