AIML Engineer - Human Perception

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, AI agents, and health applications. In 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 Apple's ecosystem.

We are looking for an AIML Engineer 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. You will not only work on cutting-edge projects to advance our AI capabilities, but also contribute to practical features in Apple products and bring impact to millions of users. You will collaborate with others to drive data requirements, validation strategies, and key performance indicators, and conduct algorithm research and development that serves product needs. A successful candidate will stay up-to-date with the latest advancements in AI, machine learning, and computer vision, applying this knowledge to drive innovation, but also take a practical approach to problem solving and software engineering to deliver clean, modular, testable code.

BS and a minimum of 3 years relevant industry experience.\nExperience building models for multimodal perception system.\nExperience working with LLMs and VLMs.\nSoftware engineering skills and proficiency in Python and PyTorch.\nCuriosity 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 fields.\nExperience in developing, training/tuning foundation models and multimodal LLMs.\nExperience with training and troubleshooting generative architectures such as diffusion, reinforcement learning, flow matching or normalizing flow at scale.\nExperience applying reinforcement learning to help train foundation models a plus.\nExcellent communication and experience working with multi-functional teams.\nSelf-motivated with proven track record to optimally prioritize and deliver tasks on schedule.