Multimodal AI Applied Scientist, SIML - ISE

Apple Inc.; Cupertino (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Apple Inc. is seeking a Multimodal AI applied scientist to drive multimodal scene understanding and human-centric device interaction.

You will develop multimodal LLMs to perceive environments and understand user behaviors, preferences, and interactions, spanning architecture design, model training, and deployment. You will work on assembling robust ML models and APIs that fit into Apple’s production pipelines, collaborating across teams to push the boundaries of foundation models and real-time

Qualifications

• PhD or MS in Computer Science, Engineering, or related field with ML focus.
• First-author publications in top-tier ML conferences.
• Expert knowledge of SOTA in large auto-regressive transformers, multimodal encoders, and representation learning.
• Experience with multimodal LLMs.
• Strong Python programming with ML code grounded in software engineering.

Responsibilities

• Design, train, and deploy large-scale multimodal LLMs, owning the lifecycle from architecture to deployment.
• Create human-centric user experiences using foundation models.
• Build robust ML models and APIs for production pipelines and training infrastructure.
• Collaborate with cross-functional teams to iterate on innovative solutions.

Skills

Research experience

Python programming

Multimodal ML

Education

PhD or MS in CS/Engineering

Job description

Summary

Apple's System Intelligence and Machine Learning (SIML) team is seeking a Multimodal AI applied scientist to drive the future of multimodal scene understanding and human-centric device interaction. At the heart of this effort, you will develop multimodal LLMs designed to perceive complex environments and understand nuanced human behaviors, preferences, and interactions. This role offers a rare opportunity to join a leading applied research team known for its foundational contributions to Apple Intelligence, where you will work across the entire generative model lifecycle, spanning novel architecture design, model training, and deployment.

Description

We are looking for an applied scientist with deep skills in ML and generative modeling to architect, train, and deploy multimodal scene understanding technology that can understand scene from visual and audio data. You will quickly build and demonstrate new user experiences using large foundation models, developing innovative capabilities while partnering with cross-functional teams and iterating rapidly in a dynamic environment.Your work will advance human interaction and scene understanding modeling across the stack, from system-level architecture to core ML algorithms. Among the many challenges you will address are modeling user behaviors and preferences through device and environmental interactions, retrieving nuanced insights from historical context, handling deeply interleaved streaming data, reasoning across diverse temporal horizons, designing memory systems for long-term adaptation, and learning semantically rich representations for open-ended downstream applications.You will also be responsible for delivering ML models and solutions that can be readily integrated into production pipelines, such as APIs for production-ready models and algorithms built directly into our training infrastructure.

Key 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.

Minimum Qualifications

• PhD or MS Computer Science, Engineering, or a related field with a focus on machine learning
• 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

• PhD in Machine Learning
• 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.