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)
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 ar...
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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