Senior Machine Learning Engineer, Apple Search & Knowledge Platforms

The AI, Search & Knowledge Platforms team builds amazing products and services for Apple's customers while serving as a foundational partner to teams across Apple. The team delivers world-class AI, search, and knowledge systems powering Siri, Apple Intelligence, Safari, and iMessage, and operates the foundational platforms and infrastructure that keep these intelligent experiences running at hyperscale.\\n\\nAs part of this group, you will be doing large scale machine learning and deep learning research and development to improve Open Domain Question Answering (using both structured knowledge graph data and unstructured web data) and Summarization as well as developing fundamental building blocks needed for Artificial Intelligence. This involves developing sophisticated machine learning and large language models (LLMs) to understand user queries, retrieve and rank relevant documents across multiple sources and synthesize information across documents to provide user with a direct answer that best satisfies their intent and information seeking needs. Additionally, you will research and develop the state-of-the-art LLMs for summarizing personal data such as emails, messages, and notifications.\\n\\nYou will also work with researchers and data scientists to develop, fine-tune, and evaluate domain specific Large Language Models for various tasks and applications in Apple's AI powered products and conduct applied research to transfer the cutting edge research in generative AI to production ready technologies.

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.

3+ 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.\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.\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