Job Description

• Rapidly prototype and benchmark multiple computer vision approaches for anomaly and defect detection, including supervised detection and classification, unsupervised and semi-supervised anomaly detection, and modern foundation model based techniques on representative inspection imagery
• Build fair and reusable evaluation frameworks so that comparisons across methods are repeatable and credible across different asset types and defect categories
• Identify top-performing methodologies and clearly document trade-offs including data requirements, performance, complexity, and inference cost
• Package the selected approach into a reusable template, including reference code, data preparation guidance, training and evaluation patterns, and supporting documentation so internal teams can extend it to additional use cases
• Collaborate closely with data scientists, domain experts, and stakeholders to ensure experiments are grounded in real inspection workflows and operational needs
• Communicate findings, recommendations, and trade-offs clearly to both technical and non-technical audiences
• Maintain thorough and organized documentation of experiments, results, and deliverables to ensure long-term usability beyond the engagement

What You Bring

• Master's degree or PhD in computer science, machine learning, computer vision, engineering, mathematics, statistics, applied sciences, or a related quantitative field, or equivalent experience
• Six or more years of experience in computer vision, machine learning, or related analytical product development, with strong emphasis on deep learning based vision systems
• Proven ability to rapidly evaluate and down-select between competing modeling approaches using fair comparisons and evidence-based conclusions
• Strong knowledge of modern computer vision techniques including object detection, classification, and at least one relevant area such as anomaly detection, few-shot learning, or vision foundation models
• Solid understanding of evaluation methods for rare-event and class-imbalanced problems
• Advanced programming skills in Python and PyTorch or similar frameworks, with experience working in version-controlled development environments
• Demonstrated ability to produce reusable, well-documented code and frameworks for other data scientists
• Strong analytical, problem-solving, communication, and documentation skills, with the ability to engage effectively across technical and business audiences

Desired Qualifications

• Experience with anomaly detection in industrial, infrastructure, or visual inspection contexts
• Familiarity with vision foundation models for transfer learning, embedding-based retrieval, or low-supervision settings
• Experience working with large-scale image datasets and cloud-based machine learning platforms such as AWS, Azure, GCP, or similar
• Background in infrastructure, industrial inspection, manufacturing quality assurance, or other environments where image-based analytics drive operational decisions

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