Machine Learning Video Algorithms Engineer
San Diego, California, United States
Imagine the impact you can make. A billion users will use the technologies you helped craft almost daily. At Apple, you will have the opportunity to work on products that are always leaders in the industry and occasionally, change the world! The Video Engineering group at Apple is responsible for crafting the image/video core technologies used in almost all Apple products and services. We are looking for a highly self-motivated and enthusiastic individual, who is able to excel in a technically meaningful environment, to fill in the position of video algorithm engineer.
- Expert knowledge of the principles, algorithms, and techniques in video and image processing, or machine learning, or computer vision technology
- Knowledge and experience in video compression standards, such as H.264, HEVC, AV1, VP9, VVC are a huge plus
- Familiarity with video processing algorithms such as tone mapping, scaling, noise reduction etc would be a plus
- Expert knowledge in digital signal processing and information theory would be a bonus
- Excellent software design, problem solving, and debugging skills
- Proven programming skills and C/C++ coding abilities
- Understanding of hardware-based techniques and able to forge ideas that will work in a hardware implementation would be a plus
- Good written and oral communication skills
In this role you will work as an individual contributor in a small and diverse team, developing video compression and processing algorithms for current and future Apple products. This position requires a highly self-directed individual with strong creative and analytic skills and passion for video processing and compression technologies. Your responsibilities include, but not limited to: 1. Invent and implement new video compression/processing algorithms that work well with existing hardware. 2. Work with other hardware/software architects to define and implement media features and algorithms for future products. 3. Spearhead R&D in learning-based and other upcoming new media technologies.
Education & Experience
MS/PhD in Electrical Engineering or Computer Science