Sr ML Research Engineer, Post-Training for Personalization

Do you believe generative models can transform creative workflows and smart assistants used by billions of people? Do you envision a future where AI fundamentally reshapes how people interact with their devices and communicate, delivering experiences personalized to their unique needs and preferences? We are part of Apple’s Intelligent System Experience team, where we develop core modeling technologies that translate cutting-edge research into compelling user experiences. Our work spans Apple Intelligence initiatives, including Personalization, Agentic Search, Generative Memories, Visual Generation, and more.

We are seeking a Staff Research Engineer with a strong track record in developing novel algorithms for LLM post-training to advance personalized intelligence. In this role, you will drive research in post-training methodologies that enable models to better understand individual user needs, adapt to personal context, and deliver more relevant, helpful, and personalized experiences. You will work at the intersection of large language models, multimodal reasoning, personalization, and agentic systems, translating emerging research into scalable, high-quality experiences.

Key attributes expected in the role is fluency in algorithm development (prompt optimization and post training), and strong expertise with relevant techniques (reinforcement learning, multimodal reasoning, context compaction / efficiency & memory systems). The role includes the opportunity to partner with world class system engineers to prototype and incorporate bleeding edge algorithmic innovations in the context of emerging agentic experiences. Ability to interface with large scale modeling & data infrastructure is a huge plus.

PhD in Electrical Engineering/Computer Science or a related field (mathematics, physics or computer engineering), with a focus on computer vision and/or machine learning or comparable professional experience.

Strong ML and Generative Modeling fundamentals.

Hands-on experience in one or more of the following areas: pre-training or post-training of large language models, and reinforcement learning.

Familiarity with distributed training.

Proficiency in using ML toolkits, e.g., PyTorch

Proven track record of research contributions demonstrated through publications in top-tier conferences, or open source contributions to algorithm.

Experience with building & deploying Multimodal-LLMs

Experience building and deploying agentic systems and LLMs with tool-use and memory capabilities