Camera Algorithms Engineer

Apple's Camera Algorithm team is looking for extraordinary engineers to work on the image capture and processing/rendering algorithms that serve all Apple product cameras! As part of the team, you will work on core camera/ISP/Machine Learning technologies based on Apple crafted image signal processing pipeline and HW components, such as the Apple Neural Engine. Here you will have the chance to define the way the Apple camera captures, processes, and renders outstanding still image and video quality. Our close-knit team fosters an environment of product innovation, rapid product iteration, and collaboration, at both team and multi-functional levels, granting you a liberating amount of autonomy. We work closely with various teams, like the Silicon Design, Camera HW, and QA. Our team environment is multifaceted, fast-paced and requires a self-starter attitude.

Description

In the algorithm developer role, you will engage tasks in one or more of following fields:

- Scene Understanding and auto exposure ISP algorithm development and ISP tuning.

- Architecture, configuration and pixel processing tuning.

- Tone rendering/grading for targeted display, and remapping between SDR and HDR displays with fidelity.

- Auto white balance and color management.

- Runtime optical flow and image registration and fusion.

- Machine learning solution development for real time image processing, with low latency and low power consumption.

Minimum Qualifications

MS degree in Electrical Engineering, Computer Science, Math, Physics or similar field.
Hands-on run time ISP algorithm development experience.
ISP HW processing pipeline knowledge.
Camera module optical stack and CMOS Bayer sensor knowledge.

Preferred Qualifications

Dedicated and passionate for image quality excellence.
Deep understanding and appreciation of sensor characteristics, optics, and image signal processing of the mobile imaging system, and their influence nuance to final image and video quality.
Devotion to disciplined and time sensible practice from problem identification to concept validation and engineering implementation with tools of theories and image processing techniques, including machine learning.