Multimodal LLMs Research Engineer
Job details
- Level
- Senior
- Experience
- 7+ years
- Education
- PhD
- Posted
- Jul 1, 2026
- Last confirmed open
- Oct 29, 2026
What you'll need to apply
What this employer's standard application typically asks
Company-specific questions
- Have you previously worked at Apple?
About this role
We are actively seeking exceptional individuals who thrive in collaborative environments and are driven to push the boundaries of what is currently achievable with multimodal inputs and large language models.
Our centralized applied research and engineering group is dedicated to developing cutting-edge Computer Vision and Machine Perception technologies across Apple products. We balance advanced research with product delivery, ensuring Apple quality and pioneering experiences.
A successful candidate will possess deep expertise and hands-on experience across the full lifecycle of Multimodal LLM development, encompassing early ideation, data definition, model training, and fine-tuning.
We are seeking a candidate with a proven track record—demonstrated through academic research, industry contributions, or a combination of both—in developing multimodal LLMs and advanced topics such as agentic AI, reasoning, and large-scale model evaluation. This role offers the opportunity to drive groundbreaking research projects, spanning foundational concepts to practical applications.
B.S. and 7+ years of relevant industry experience Demonstrated track record through publications, patents, and/or shipping relevant features Strong Python programming experience Strong PyTorch and/or JAX programming experience Ability to effectively utilize AI code development tools to accelerate the development process
M.S. and 5+ years of relevant industry experience or Ph.D. and 3+ years of relevant industry experience Strong publication record in relevant venues, such as CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, etc. Technical leadership experience - guiding technical efforts across diverse teams/individuals. Experience in shipping MM-LLMs in products.