Principal Computer Vision Scientist

About the role

This role is part of the Global Engineering Organization (GEO) at Sourceability®, a global digital distributor of electronic components. The role focuses on leading advanced Computer Vision and AI/ML work within GEO, with responsibilities including research direction, model architecture, experimentation, model quality, and production readiness.

Responsibilities

• 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.
• 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.

Requirements

The ideal candidate should have a PhD in Computer Science, Computer Vision, Machine Learning, Artificial Intelligence, Applied Mathematics, Electrical Engineering, Robotics, or a closely related technical field. They should have 7+ years of hands-on experience in Machine Learning/Deep Learning, with a strong focus on Computer Vision. Strong practical experience with PyTorch and/or TensorFlow, and strong Python development skills are required. Experience with OpenCV, NumPy, Pandas, scikit-learn, and modern Python ML ecosystem is essential. A deep understanding of classical Computer Vision algorithms and modern deep learning approaches is necessary, along with 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, is preferred. Experience bringing ML models into production environments, optimizing for inference speed, latency, memory usage, scalability, and reliability, and working with REST APIs, Docker, CI/CD, model versioning, experiment tracking, and MLOps practices is crucial. Strong understanding of datasets, data quality, annotation processes, labeling requirements, and model error analysis is expected. Ability to read, understand, and evaluate technical documentation and research papers in English, and to explain complex technical topics to engineering, product, and business stakeholders, is a must-have. Experience working in Agile software development environments and being comfortable working in distributed teams across multiple locations and time zones is preferred.

Qualifications

• 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 a 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.
• A 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 requirements, and model error analysis.
• Ability to read, understand, and evaluate technical documentation and research papers in English.
• Ability to explain complex technical topics to engineering, product, and business stakeholders.
• Experience working in Agile software development environment.
• Strong ownership mindset, good judgment, and ability to make practical technical decisions under uncertainty.
• Comfortable working in distributed teams across multiple locations and time zones.

Skills

• Post-PhD research or industry experience in applied Computer Vision.
• Publications, patents, or strong applied research record in Computer Vision, Machine Learning, or AI.
• Experience leading technical direction for AI/ML projects.
• Experience mentoring ML engineers, software engineers, or data annotation teams.
• Experience with edge or mobile inference technologies, including ONNX, TensorRT, OpenVINO, TFLite, CoreML, or similar.
• Experience with large-scale image processing pipelines.
• Experience with synthetic data generation, active learning, weak supervision, or dataset quality improvement.
• Experience with multimodal models, vision-language models, prompt engineering, OpenAI, or similar AI platforms.
• Experience with cloud ML platforms and production monitoring of ML models.
• Familiarity with mobile applications, warehouse workflows, field operations systems, or image capture workflows.
• Familiarity with Azure DevOps, Git, CI/CD tooling, documentation systems, and practical software delivery processes.
• Experience in electronic components, technology distribution, supply chain, logistics, manufacturing, e-commerce, or similar B2B environments.

Benefits

Sourceability® offers competitive compensation packages, comprehensive benefits, and opportunities for professional growth and development. The company values diversity and inclusion, providing a supportive and inclusive workplace for all employees.

Pay

Compensation for this role is competitive and commensurate with experience and qualifications. Details will be provided during the interview process.

Schedule

The schedule for this role is flexible and aligns with the needs of the team and the company. Remote work is possible, but regular office presence is encouraged.