Principal Computer Vision Scientist

Sourceability is building a new Global Engineering Organization (GEO) to strengthen internal software delivery, improve production ownership, and build long-term engineering capability inside the company. We are looking for a Principal Computer Vision Scientist to lead advanced Computer Vision and AI / ML work inside GEO. This role will be responsible for research direction, model architecture, experimentation, model quality, production readiness, and practical implementation of computer vision solutions used in company products. This is a senior technical leadership role for a highly experienced specialist who can work across research, engineering, product, and production systems. The right candidate should be able to evaluate new approaches, design model architectures, run experiments, improve model quality, and help engineering teams bring AI / ML capabilities into real production workflows. This role requires PhD-level education and strong hands-on experience in applied Computer Vision, Machine Learning, and Deep Learning. The person in this role should be comfortable working with business-critical systems, practical production constraints, imperfect datasets, and evolving product requirements. Assigned Product Group This role will be primarily aligned with the Computer Vision product group inside GEO. The role may also support other internal product groups or AI / ML initiatives where computer vision, image processing, visual search, object detection, segmentation, classification, or model evaluation expertise is needed. Product Group Focus Areas Depending on business priorities, the role may focus on one or more of the following areas: • Mobile App / Computer Vision: Image capture workflows, mobile application integration, computer vision model development, model inference, warehouse / field usability, user feedback loops, production model quality, and UAT support. • AI / ML Product Features: Applied machine learning features, model evaluation workflows, proof-of-concepts, model-assisted automation, AI-assisted business tools, and integration of AI / ML capabilities into existing business workflows. • Data and Annotation Workflows: Image datasets, data quality, annotation requirements, labeling guidelines, model training datasets, validation datasets, failure case analysis, and continuous improvement of model performance. • Production ML Systems: Model deployment, model versioning, inference performance, monitoring, reproducibility, scalability, reliability, and MLOps practices. Insight on Your Impact In this role, you will: • Lead research, design, development, and implementation of Computer Vision and AI / ML solutions. • Define model architecture, technical approach, experiment strategy, validation methodology, and production readiness criteria. • Train, fine-tune, evaluate, optimize, and deploy models for object detection, semantic segmentation, image classification, feature matching, OCR, visual search, and image understanding. • Own the full model lifecycle, including data analysis, dataset quality, annotation requirements, model training, experiment tracking, evaluation, deployment, monitoring, and continuous improvement. • Build prototypes, proof-of-concepts, demos, and technical experiments to validate new ideas before full product implementation. • Analyze model performance, identify failure cases, and recommend practical improvements based on data, user behavior, and business needs. • Review and improve existing Computer Vision pipelines, model quality, inference performance, scalability, and production reliability. • Work with software engineers to integrate ML models into production applications and services. • Define standards for model evaluation, model versioning, dataset management, reproducibility, and MLOps practices. • Evaluate research papers, open-source models, AI platforms, and new technologies for potential use in company products. • Provide technical guidance and mentoring to engineers working on AI / ML and computer vision features. • Support planning and estimation for AI / ML work by clarifying technical complexity, risks, dependencies, and realistic delivery assumptions. • Create technical documentation, model evaluation reports, architecture notes, and recommendations for engineering and product teams. • Partner with Product / Delivery Managers to translate business needs into practical AI / ML implementation plans. • Partner with Engineering Managers, Team Leads / Architects, QA, DevOps, Data, and business stakeholders to make sure AI / ML work can be delivered and supported in production. Your Qualifications, Your Influence To be successful in this role, you should have: • PhD in Computer Science, Computer Vision, Machine Learning, Artificial Intelligence, Applied Mathematics, Electrical Engineering, Robotics, or closely related technical field. • 7+ years of hands-on experience in Machine Learning / Deep Learning, with strong focus on Computer Vision. • Strong practical experience with PyTorch and / or TensorFlow. • Strong Python development skills. • Experience with OpenCV, NumPy, Pandas, scikit-learn, and modern Python ML ecosystem. • Deep understanding of classical Computer Vision algorithms and modern deep learning approaches. • Strong experience with object detection, semantic segmentation, image classification, feature matching, image retrieval, and model evaluation. • Experience with modern Computer Vision architectures and techniques, including CNNs, Transformers, Vision Transformers, YOLO, Mask R-CNN, CLIP-like models, SAM-like models, or similar. • Experience bringing ML models into production environments. • Experience with model optimization for inference speed, latency, memory usage, scalability, and reliability. • Experience with REST APIs, Docker, CI / CD, model versioning, experiment tracking, and MLOps practices. • Strong understanding of datasets, data quality, annotation processes, labeling requir…