Multimodal AI Researcher

The Video Computer Vision organization is working on breakthrough technologies for future Apple products. Our team delivers cutting-edge AI machine learning computer vision and graphics algorithms that power technologies including human understanding perception digital humans multimodal generative AI and agents. Our algorithms ship across a range of Apple products including iPhone and Apple Vision Pro where our work has contributed to technologies like Personalized Spatial Audio EyeSight and Persona as well as future Apple products. We are an applied research group we push the state of the art and then bring it to this role you will collaborate with world-class experts in AI ML Software and Hardware to tackle fundamental challenges in human-centric solutions that will impact millions of users across Apples ecosystem.

We are looking for a Multimodal AI Researcher with a strong background in developing foundation models for generative AI and multimodal systems that integrate various types of real-time sensor data such as video and audio with other modalities like text. Our ongoing investigations include interactive models audio-to-audio modeling and systems. You will work on hard open research problems in multimodal generative AI and agents and you will see that work through to real features used by millions of people. You will collaborate with others to drive data requirements validation strategies and key performance indicators and conduct algorithm research and development that serves product hire researchers who are highly motivated and deeply care about shipping. A successful candidate will stay up-to-date with the latest advancements in multimodal foundations models and applying this knowledge to drive innovation but also take a practical approach to problem solving and software engineering.

BS and a minimum of 3 years relevant industry building models for multimodal perception working with LLMs and engineering skills and proficiency in Python and and willingness to learn new things in order to improve the quality of their solutions.

MS or PhD in computer vision computer graphics machine learning computer science computer engineering or related in developing training/tuning foundation models and multimodal with training and troubleshooting generative architectures such as diffusion reinforcement learning flow matching or normalizing flow at with real-time or streaming multimodal with speech understanding and applying reinforcement learning to help post-train foundation communication and experience working with multi-functional -motivated with proven track record to optimally prioritize and deliver tasks on schedule.