Apple is looking to shape how devices of the future perceive, understand, and anticipate human interactions by inventing and developing techniques for modeling natural human interactions with objects, devices, and humans.
Requirements
- Expert-level knowledge of SOTA in large auto-regressive transformer models, multi-modal encoders, and representation learning.
- Strong programming skills in Python, maintaining ML code bases grounded in software engineering principles.
- Experience with multimodal large language models (LLMs).
- Familiarity with developing ML for resource-constrained devices.
- Proven experience building and deploying large ML models
Responsibilities
- Invent and develop techniques for modeling natural human interactions with objects, devices, and humans.
- Define project goals, work with cross-functional partners, learn quickly, and implement and demonstrate new user experiences using large Foundation models.
- Build novel and innovative technology, forge collaborations, and influence multi-functional partners, and adapt and iterate your solutions in a dynamic environment.
- Advance human interaction modeling across various fronts, at a system level, and at a core ML algorithm level.
- Perceiving subtle cues in gaze, affect, or prosody, handling deeply interleaved streaming inputs, reasoning over varying temporal contexts, developing memory systems to enable long-term adaptation, generating semantically rich internal representations to enable open-ended downstream tasks.
- Delivering solutions that can readily be adopted in production pipelines, such as APIs for production-ready ML models or algorithms well integrated into training infrastructure.
- Proven experience building and deploying large ML models
Other
- PhD or MS in Computer Science, Engineering, or a related field with a focus on machine learning; or equivalent experience
- Strong research skills with publications in top tier ML conferences
- Experience working with large cross-functional and diverse teams.
- Proven track record of deploying innovative ML technologies in production.
- Publications in peer-reviewed venues in relevant areas.
