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.

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

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

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