Job Description

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 workstreams, tool calling, context compaction / efficiency & 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) https://arxiv.org/pdf/2507.13575
(b) https://arxiv.org/pdf/2407.21075
(c) https://www.apple.com/newsroom/2024/12/apple-intelligence-now-features-image-playground-genmoji-and-more/

We are seeking 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 & 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 & data infrastructure is a huge plus.

Preferred Qualifications

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

Minimum Qualifications

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
3+ years of experience leading and growing high-performing machine learning teams of individual contributors.
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