Display Algorithm Engineer

Apple is looking for a Display Algorithm Engineer to drive the development of advanced display technologies. This role focuses on innovative display algorithms and machine learning models that optimize and enhance Apple display products across all platforms. We want for someone passionate about analyzing engineering problems with data and delivering algorithms that achieve optimal performance.

As a member of the Display Algorithm team, you will collaborate with teams across Apple that are building the most advanced display products in the world. You will develop algorithms grounded in machine learning models and large-scale data analysis, starting early in a product"s lifecycle to identify potential challenges and their solutions. You will translate cross-functional insights and data into algorithms and models ready for implementation.

Investigate emerging display technologies and develop new calibration approaches and test plans to improve display optical performance\\nDevelop identification and classification models, along with image processing methods for defect inspection, to quantify front-of-screen display performance\\nCollaborate with multi-disciplinary teams across display module, manufacturing, test, and system engineering, providing technical leadership and expertise to initiate new project solutions\\nPartner with the Display Module team to analyze measurement data and develop the computer vision and machine learning models needed to understand, optimize, and resolve display-related problems\\nCharacterize display failures, capture the datasets required, and design machine learning experiments using the appropriate ML libraries

M.S. in Electrical Engineering, Computer Science, Optics, Physics, or a related quantitative field with 3+ years of industry experience (or equivalent practical experience)\\nProficiency in Python and fundamental machine learning concepts, with hands-on experience using frameworks such as PyTorch, TensorFlow/JAX, or Scikit-learn\\nExperience with the ML lifecycle: data curation/cleaning, model exploration, training/fine-tuning, and systematic evaluation\\nWorking knowledge of digital image processing, and computer vision techniques

Ph.D. in Electrical Engineering, Optics, Physics, or Computer Engineering with expertise in image processing and machine learning\\nExperience with multimodal machine learning (e.g., VLM), including adapting or fine-tuning large pre-trained multimodal models for domain-specific or scientific imagery\\nStrong domain background in display technologies (e.g., OLED, LCD), display systems and driving, optical metrology/calibration, or color science\\nTrack record of bridging physical/hardware domain knowledge with data-driven ML models\\nFirst-author publication record in peer-reviewed ML or engineering venues (e.g., NeurIPS, ICML, ICLR, CVPR, IEEE, or equivalent)\\nExcellent cross-functional communication and collaboration skills to drive technical solutions across hardware, software, and display engineering teams