Multimodal ML Engineer — On-Device AI & Vision (Seattle)

Apple Inc.; Seattle (WA); Híbrido; USD 142 000 - 263 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Medical & Dental coverage

Retirement benefits

Product discounts & free services

Education reimbursement

Relocation assistance

Stock programs

Resumo da oferta

Apple Inc. in Seattle, WA is seeking a Machine Learning Engineer to build end-to-end pipelines and production systems that turn multimodal foundation models into shipping Apple Intelligence features.

You will own data curation, training pipelines, fine-tuning, evaluation, and deployment under real latency and privacy constraints, collaborating across modeling, platform, and hardware teams to deliver market-leading AI experiences.

Qualificações

• Experience in deep learning with multimodal systems (vision/lang/video/audio).
• Proficiency in Python and PyTorch or JAX.
• Experience with rapid prototyping, reproduction, and validation of research ideas.
• Ability to work in a collaborative environment.
• Ability to communicate analysis results clearly.
• BS and a minimum of 3 years of relevant industry experience.

Responsabilidades

• Build pipelines, infrastructure and production systems for multimodal foundation models.
• Own end-to-end model delivery, including data curation and training pipelines.
• Fine-tune and optimize models for on-device and hybrid execution.
• Set up reproducible evaluation and regression testing for text and visual understanding.
• Collaborate with modeling, platform, hardware and product engineering teams.

Conhecimentos

Multimodal ML

Python

PyTorch/JAX

Research to production

Collaboration

Communication

BS + 3 years

Formação académica

Bachelor's degree minimum

Master's/PhD preferred

Descrição da oferta de emprego

Apple Inc. in Seattle, WA is seeking a Machine Learning Engineer to build end-to-end pipelines and production systems that turn multimodal foundation models into shipping Apple Intelligence features.

You will own data curation, training pipelines, fine-tuning, evaluation, and deployment under real latency and privacy constraints, collaborating across modeling, platform, and hardware teams to deliver market-leading AI experiences.