Generative AI Applied Scientist, SIML - ISE

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.

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.

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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