Generative AI Applied Scientist, SIML - ISE Summary Apple's System Intelligence and Machine Learning (SIML) team is seeking a senior Generative AI expert to pioneer the next generation of human-centric device interaction and multimodal scene understanding. You will be at the core of our efforts to develop multimodal LLMs that can perceive and understand complex scenes and nuanced human interactions, behaviors, and preferences. This is a unique opportunity to join a leading applied research group known for its foundational contributions to Apple Intelligence, where you will focus on the end-to-end lifecycle of generative models—from novel architecture design and large-scale training to final deployment. Responsibilities Design, train, and deploy large-scale multimodal LLMs, owning the entire lifecycle from initial architecture to final deployment within the constraints Implement and demonstrate novel, human-centric user experiences by applying the capabilities of large foundation models Create robust, scalable ML models and APIs that can be well-integrated into Apple's production pipelines and training infrastructure Work closely with partner teams to build, iterate, and adapt innovative solutions in a dynamic, product-focused environment

Description

Description We are looking for a senior applied scientist with strong ML and Generative modeling skills who can design, train, and deploy multimodal GenAI technology. You will need to learn quickly and implement and demonstrate new user experiences using large foundation models. You will build novel and innovative technology, forge collaborations with cross-functional partners, and adapt and iterate your solutions in a dynamic environment. You will be expected to advance human interaction and scene understanding modeling across various fronts, from a system level to a core ML algorithm level. Some of the myriad challenges include understanding user behavior and preferences from interactions with the device and the environment, retrieving useful and nuanced information based on past interactions, handling deeply interleaved streaming inputs, reasoning over varying temporal contexts, developing memory systems to enable long-term adaptation, and generating semantically rich internal representations to enable open-ended downstream tasks. You will be responsible for delivering ML models and solutions that can readily be adopted in production pipelines, such as APIs for production-ready ML models and algorithms well-integrated into our training infrastructure.

Minimum Qualifications

Minimum Qualifications PhD or Masters Degree in Computer Science, Engineering, or a related field with a focus on machine learning; or equivalent experience Strong research skills with first author publications in top tier ML conferences Expert-level knowledge of SOTA in large auto-regressive transformer models, multi-modal encoders, and representation learning Experience with multimodal large language models (LLMs) Strong programming skills in Python, maintaining ML code bases grounded in software engineering principles

Preferred Qualifications

Preferred Qualifications Proven track record of deploying innovative ML technologies in production Familiarity with developing ML for resource-constrained devices Experience working with large cross-functional and diverse teams

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