Senior Multimodal ML Research Lead

Apple, Inc.; Seattle (WA); On-site; USD 205,000 - 309,000; Full time

Benefits offered by this job

Apple Benefits

Discretionary bonuses and stock plans

Relocation assistance

Job summary

Apple, Inc. in Seattle, WA is seeking a senior technical leader to architect and deploy production-scale multimodal ML. You will lead cross-functional efforts across ML modeling, prototyping, validation, and private learning, with emphasis on integrating NLP and vision capabilities.

The role requires hands-on experience with training/adapting LLMs and collaboration with system engineers to prototype bleeding-edge algorithms at scale.

Qualifications

• PhD with 5+ years experience or MS with equivalent experience in Computer Science/Electrical Engineering or related field.
• Hands-on experience training LLMs/adapting pre-trained LLMs for downstream tasks & alignment.
• Modeling experience at the intersection of NLP and Vision.
• Proficiency in ML toolkit of choice, e.g., PyTorch.
• Strong programming skills in Python.
• Proven track record of research contributions through publications or open source contributions.

Responsibilities

• Architect and deploy production-scale multimodal ML.
• Lead cross-functional efforts spanning ML modeling, prototyping, validation and private learning.
• Interface with large-scale modeling and data infrastructure.
• Partner with system engineers to prototype and incorporate advanced algorithmic innovations.

Skills

LLM training

NLP + Vision modeling

PyTorch

Python

Publications / Open source

Education

PhD in CS/EE

MS in CS/related field

Tools

PyTorch

Job description

Apple, Inc. in Seattle, WA is seeking a senior technical leader to architect and deploy production-scale multimodal ML. You will lead cross-functional efforts across ML modeling, prototyping, validation, and private learning, with emphasis on integrating NLP and vision capabilities.

The role requires hands-on experience with training/adapting LLMs and collaboration with system engineers to prototype bleeding-edge algorithms at scale.