Senior Research Manager, Multimodal Reasoning - SIML

The System Intelligence and Machine Learning (SIML) Content Understanding team is seeking a Senior Research Manager in Multimodal Reasoning. You will be working alonside teams that are in charge of operating system wide embeddings, personalized RAG work streams, tool calling, context compaction / efficiency u0026 memory systems. Projects are focussed on advancing Apple Intelligence capabilities, while working closely across disciplines with our partners in hardware engineering, design and product.

Selected references to our prior work (a) (Use the "Apply for this Job" box below). (b)  (c)

We are looking for a senior applied research leader in multimodal reasoning. The role expects fluency in algorithm development and computation, ability to set up future facing research investigations, and leading cross functional teams on company wide efforts. Key attributes expected in the role is fluency with state of art LLM workflows (prompt optimization, post training / alignment, automatic evaluation, distributed training u0026 inference).

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 u0026 data infrastructure is a huge plus.

Experienced in leading applied research efforts in Multimodal-LLMs Track Record in transitioning applied research initiatives to customer facing experiences Familiarity with distributed training and large-scale data infrastructure

PhD, or MSc in Computer Science/Electrical Engineering, or a related field (mathematics, physics or computer engineering); with a focus on machine learning, or comparable professional experience Strong ML and Generative Modeling fundamentals Strong expertise in one of the following:
Reinforcement Learning, Multimodal Training, Pre-training / Post-training foundation models 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