Senior Computer Vision Architect

The Video Computer Vision organization within Video Engineering is a centralized applied research and engineering entity responsible for developing real-time on-device Computer Vision and Machine Perception technologies for Apple products. We strike a balance between research and product development to deliver Apple-quality state-of-the-art experiences. We innovate across the full stack and collaborate with hardware software and machine learning teams to influence their roadmaps and enable Apples future product development.

The Architecture team within our organization brings together multiple computer vision algorithms and AI/ML teams to drive some of the most exciting programs across Apple products. We achieve this by enabling co-design of computer vision algorithms and systems through a deep understanding of the complexities and tradeoffs. Success in this role requires not only exceptional engineering skills but also the ability to collaborate effectively. To be successful in this role you not only have to be an excellent engineer but also a great collaborator comfortable communicating with a wide range of experts and leaders across many different domains. Are you ready to be a part of the next big thing at Apple

Proven track record of leading cross-functional initiatives that have successfully transformed state-of-the-art computer vision research into production-ready product mindset focused on architecting technical solutions that solve real business problems and deliver high customer understanding of multi-modal perception systems sensor fusion and diverse imaging technologies is degree in Computer Science Electrical Engineering or a related field coupled with a minimum of 10 years of relevant proven experience is required.

Proven track record of designing custom silicon sensors or leadership skills are essential to drive large cross-functional efforts resolve conflicts and present results to the highest levels of Apple leadership. This includes demonstrating the ability to bridge the gap between high-level algorithm design and low-level system optimization particularly in the challenging environment of optimizing image signal processing (ISP) performance under tight power and latency candidates should also have exposure to modern innovative techniques such as vision transformers foundation models and vision-language models. These candidates should have a strong focus on integrating these methods into resource-constrained leading real-time computer vision projects (e.g. SLAM calibration scene understanding gaze tracking human representation and activity recognition) or low-level software development for ISP organizations.