We are looking for an innovative and highly skilled Computer Vision Engineer with expertise in developing image quality algorithms for mobile platforms. The ideal candidate will have a deep understanding of image processing, computer vision, and AI-based enhancement techniques, with proven experience in creating and optimizing algorithms such as HDR, denoising, and deblurring for smartphone photography.
This role offers the opportunity to work at the intersection of cutting-edge research and real-world application, designing solutions that enhance image quality while balancing performance and efficiency on mobile devices.

QUALIFICATIONS

• Education: Master’s degree or higher in Computer Vision, Image Processing, Deep Learning, or a related field.
• Solid understanding of YUV and RAW image formats, common image processing algorithms, and ISP pipelines.
• Proficiency in C/C++ and Python programming.
• Experience with deep learning algorithms and frameworks such as PyTorch or TensorFlow (TensorFlow Lite experience is a plus).
• Familiarity with image processing libraries such as OpenCV, Pillow (PIL), and scikit-image.
• Strong problem-solving, analytical, and communication skills; ability to work effectively in cross-functional teams.
• Preferred: Research or practical experience with generative AI techniques (e.g., Stable Diffusion, GANs, ControlNet, LoRA) for image quality enhancement.

TECHNICAL SKILLS

• Programming Languages: C/C++, Python
• Deep Learning Frameworks: PyTorch, TensorFlow, TensorFlow Lite
• Image Processing Libraries: OpenCV, Pillow (PIL), scikit-image
• Tools & Platforms: Git, GitHub/GitLab, Docker

• Research, design, and develop advanced algorithms for HDR, denoising, and deblurring to optimize image dynamic range, tonal accuracy, and fine detail reproduction.
• Develop efficient traditional algorithms tailored to mobile platform constraints, optimizing for computational speed, memory usage, and battery consumption.
• Integrate algorithms into ISP (Image Signal Processing) pipelines to enhance smartphone photography under diverse lighting and motion conditions.
• Solve real-world imaging challenges, including noise suppression, motion artifacts, and color consistency across scenes.
• Collaborate with hardware and software teams from algorithm prototyping to full product deployment, ensuring smooth integration and maximum efficiency.
• Combine traditional image processing methods with AI-based models to address complex, real-world imaging problems.