Apple Inc.-posted about 1 month ago
Full-time • Senior
Sunnyvale, CA
5,001-10,000 employees
Computer and Electronic Product Manufacturing

We're starting to see the incredible potential of multimodal foundation and large language models, and many applications in the computer vision and machine learning domain that previously appeared unfeasible are now within reach. We are looking for a highly motivated and skilled Senior Software Engineer to join our team in the Video Computer Vision group and help us enable that potential for realtime human understanding on Apple devices. The Video Computer Vision org has pioneered human-centric real-time features such as FaceID, FaceKit, and Gaze and Hand gesture control which have changed the way millions of users interact with their devices. We balance research and product requirements to deliver Apple quality, pioneering experiences, innovating through the full stack, and partnering with HW, SW and AI teams to shape Apple's products and bring our vision to life.You'll work on ground breaking projects to advance our AI and computer vision capabilities for human understanding. You have a strong background in integrating CV/ML algorithms in your code and efficiently running foundation and language models on device. You'll have the opportunity to collaborate with multi-functional teams, including researchers, data scientists, software engineers, human interface designers and application domain experts.

  • Experience building internal developer tools: Hands-on experience developing tools such as test data visualization systems, debugging enhancements, and robust unit/integration testing frameworks to support engineering workflows.
  • Experience with live camera streaming applications: Understanding of real-time video pipelines, image transformations, and rendering loops.
  • Experience integrating on-device CV/ML algorithms: Familiarity with common computer vision techniques (e.g., object detection, segmentation, tracking, pose estimation), sequence models for real-time inference and FMs/LLMs optimized for on-device performance.
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