Job Description

Apple is where individual imaginations gather together, contributing to the values that lead to great work. Every 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. You will lead the strong team of MLE, SWE, and data engineers responsible for delivering efficient and effective Generative AI models to build and improve the summarization capabilities across different data types.

In this role, you'll drive E2E R&D and engineering to generate high-quality summaries and experiences for Apple users. This includes on-device LLM models for personal content summarization across 1P and 3P apps, and powerful summarization models on Apple’s Private Cloud Compute servers. In addition, improve the summarization models’ quality for world knowledge-seeking questions and Safari pages to provide accurate answers and highlight web page gists in real-time. Lead the team to develop SOTA LLM-based generative models, groundedness models, and safety models for accurate, grounded, concise, and safe summaries. Develop sophisticated on-device and on-server software frameworks for context integration fast and cost-efficient LLM-based model inference. Integrate the Apple ecosystem with Apple’s LLM infrastructure and generative models to deliver delightful user experiences. Devise the product vision and strategy and execute the plan to deliver the highest quality end-user experience. Collaborate with various organizational partners to profoundly impact billions of Apple users worldwide.

Preferred Qualifications

Strong background and experience in Machine Learning, NLP, and RAG.
Strong engineering and R&D experience in LLM post-training, advanced RL-based methods to improve LLM models’ safety and quality using RLHF/RLAIF, reward model, advanced RL policy optimization algorithms, cutting-edge hallucination reduction methods, and their engineering implementation, hands-on experience to develop and ship RL based models with high availability, low latency, robustness, and stability.
Exceptional verbal and written communication skills to lead
Excellent product vision and sound business acumen. Ability to manage long-term strategy and short-term deliverables.
Strong engineering leadership and fundamentals.

Minimum Qualifications

MS or Ph.D. in Computer Science, Machine Learning, information retrieval, data mining, or a related field
8+ years of experience in leading engineering/applied research/ML experiences in natural language processing, SOTA generative AI models
Proven record of consistent delivery of technology/products across the full Machine Learning life cycle