GPU Image Processing Framework Software Engineer

Summary

iPhone is the most popular camera in the world, and at the heart of the image quality and effects that have made it so sits Core Image, Apple’s industry-leading GPU-centric image processing framework. Join us and help drive phenomenal and groundbreaking imaging capabilities across iOS and macOS, powering leading mobile and desktop technologies from iPhone's Portrait Mode and Stage Light to the Mac's iMovie, Photos, Pixelmator, and top third-party image editing applications, collaborating with teams across the company and beyond, and directly impacting the millions of photos captured across the Apple ecosystem every day. Imagine what you could do here with your abilities and expertise.

Minimum Qualifications

Proficiency in GPU programming. Proficiency in C/C++ Programming. Experience or coursework in performance programming and systems programming; concurrency and memory-constrained programming knowledge is a plus. Experience or coursework in image processing programming. Good interpersonal skills and the ability to work effectively in cross-functional teams. Bachelor's Degree in Computer Science or related discipline, or equivalent experience.

Preferred Qualifications

Experience with UNIX/Linux highly desired; experience with OS X/Darwin a major plus. Familiarity with raw image processing, demosaicking, and common image manipulation is a plus. Familiarity with color spaces, pixel representations, and dynamic range is desired.

Description

We are looking for a software engineer to join the Apple Camera and Photos team to help develop the next generation GPU-based imaging processing technologies on mobile and desktop platforms, with emphasis on performance optimization and machine-learning integration. You will play a meaningful role in adding new capabilities and optimizing performance for industry-leading image processing frameworks.

Responsibilities

Responsibilities include collaborating with teams across and beyond the company to develop new algorithms for future initiatives in computational photography, identify key use cases for performance maximization, analyzing and investigating the performance of image processing pipelines, identifying bottlenecks and opportunities for performance improvements, designing and extending efficient processing pipelines, and implementing optimized code paths for the best results.