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In this role, you will work on LLM based question answering and Apple Intelligence features to provide concise, accurate, and grounded information to users to help them complete their tasks quickly on Apple devices. \n\nYour core responsibilities will include:\n\n* Designing and developing advanced Reinforcement Learning technologies in the post-training of generative model, and delivering the end-user experience.\n* Driving cross-functional technical initiatives, collaborating with research, engineering and production teams to translate theoretical advances into deployable systems.\n* Developing novel and cutting-edge RL algorithms and improving existing ones.\n* Staying up to date with the latest RL research and integrate best practices into the team's workflow.\n* Working on the end-to-end ML lifecycle: algorithm design and implementation, data collection, model training, evaluation, and deployment.
1+ years of ML experiences in search, natural language processing/understanding. Conversational AI.\nProven experience for LLM post training, including but not limited to SFT, RLHF, RLAIF, Reward Modeling, Chain-of-thought, agentic LLM.\nHands-on experience building RL pipelines and training agents in simulation or real-world environments.\nExperienced researcher in areas of machine learning, including natural language or speech\nGrowth mindset and ability to learn new technologies\nMS or Ph.D. in Computer Science, Machine Learning with a specialty in reinforcement learning, or a related field
Deep expertise in reinforcement learning-based post-training on LLM models, reward modeling, RLHF, RLAIF, Chain-of-thought, and agentic AI R&D.\nExperienced researcher with publications in areas of machine learning, including natural language processing \nDeep understanding of cutting edge RL algorithms and large language model.\nDeep understanding in LLM pre-training, post-training.\nStrong product intuition and ownership\nExcellent communication skills
Compétences linguistiques
- English
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