Computer Vision Engineer (Grading Applications)
Job Description
Factored was conceived in Palo Alto, California by Andrew Ng and a team of highly experienced AI researchers, educators, and engineers to help address the significant shortage of qualified AI & Machine-Learning engineers globally. We know that exceptional technical aptitude, intelligence, communication skills, and passion are equally distributed around the world, and we are very committed to testing, vetting, and nurturing the most talented engineers for our program and on behalf of our clients.
We are looking for a hands-on, experienced Computer Vision Engineer to take the lead in developing cutting-edge visual inspection systems for automated cosmetic grading of high-resolution images. You’ll be at the forefront of AI development for a highly nuanced problem: detecting and classifying subtle visual defects like scratches, cracks, and blemishes on mobile devices — while tackling challenges like debris, lighting inconsistencies, and subjective human labels.
Functional Responsibilities:
- Design, train, and deploy deep learning models (e.g., CNNs) for fine-grained visual inspection and defect detection tasks.
- Apply advanced image analysis techniques such as segmentation, feature extraction, and contrast enhancement to improve model accuracy and defect visibility.
- Build Cosmetic Grading models to classify surface conditions and assign quality grades in accordance with cosmetic standards and internal benchmarks.
- Develop techniques to address inconsistencies in human annotations using: Label smoothing/ soft labeling, consensus modeling, uncertainty quantification or Human-in-the-loop feedback.
- Architect and manage image data pipelines from acquisition to annotation and model training.
- Work closely with software engineers, QA teams, and hardware support teams to integrate models into production workflows.
Qualifications:
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- 4+ years of hands-on experience developing and deploying computer vision models in real-world applications.
- Strong experience in cosmetic or defect grading applications, especially in manufacturing, logistics, or visual inspection environments.
- Proven track record working with image classification, segmentation, and object detection.
- Solid Python skills and experience with deep learning frameworks (PyTorch, TensorFlow).
- Strong understanding of image preprocessing methods (e.g., normalization, augmentation, filtering).
- Experience working on cloud platforms, preferably AWS.
- Proven ability to collaborate with multidisciplinary teams (software, hardware, product).
- Excellent verbal and written communication skills in English.
