About the job
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 infeasible 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.
Responsibilities
Develop applications and tools for algorithm evaluation, prototyping, and technology demonstrations.
Support teams across Apple by integrating ML and CV components into production systems.
Advocate for engineering excellence, code quality, thorough test suites, and long-term maintainability.
Qualifications
Minimum
Track record of multi-functional collaboration and product delivery: Demonstrated success delivering high-performance, production-quality code in collaborative, multi-disciplinary environments.
Bachelor's degree in Computer Science or related discipline, and 5 years relevant industry experience.
Strong foundational knowledge in Computer Science.
Extensive programming experience in Python.
Hands-on experience with cloud providers (AWS, GCP, or Azure).
Strong understanding of core infrastructure concepts (e.g., compute, networking, storage, containers, Kubernetes).
Foundational understanding of machine learning: Solid grasp of ML algorithms and development pipelines, with the ability to work effectively with ML practitioners and integrate ML components into production systems.
Preferred
Experience with machine learning model development lifecycle, including data preprocessing, model training, evaluation, and deployment.
Proficiency with cloud computing and distributed data processing infrastructure and tools (e.g., Ray, Spark, Trino).
Hands-on experience with CI/CD pipelines and practices.
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 LLMs optimized for on-device performance.