Apple's Hardware Technologies team develops the innovative displays that define our products and delight our customers worldwide. The Panel Technologies team is at the forefront of developing display technology that pushes the boundaries of what's possible. We work on the complex algorithms and systems that make Apple's displays not just beautiful, but reliable and long-lasting. Join our team and help create the generation of display experiences that will wow customers around the globe! Our work centers on the intricate relationship between OLED (organic light-emitting diodes) and thin-film transistor backplanes in modern displays. These complex interactions require deep technical understanding and innovative algorithmic solutions. As an Algorithm Engineer on our team, you will develop the algorithms that enable breakthrough display designs while ensuring both performance and reliability.This role requires someone who thrives on complex technical challenges without precedents. You will need understanding of sophisticated electro-optical systems and translate that knowledge into practical algorithmic solutions. This position breaks down multifaceted display physics problems into solvable components, architecting elegant compensation algorithms, and implementing solutions that balance cutting-edge performance with product longevity. Strong analytical thinking and creative problem-solving are essential. We require expertise in data processing pipelines, visualization tools, and statistical frameworks to extract meaningful insights from complex display performance datasets. Working hands-on with relevant equipment is the starting point of the investigation. Collaboration is key to success in this role. Display compensation algorithm development requires close coordination with counterparts across Display Engineering, Silicon design, Software Engineering, and manufacturing partners. The role demands both depth in display physics and communicating complex concepts across diverse engineering teams. Communicating technical findings to various audiences is needed while translating complex algorithmic concepts into actionable engineering decisions.