Principal Computer Vision Scientist

Sourceability

About The Position

Sourceability is seeking a Principal Computer Vision Scientist to lead advanced Computer Vision and AI / ML initiatives within its new Global Engineering Organization (GEO). This role is crucial for strengthening internal software delivery, enhancing production ownership, and building long-term engineering capabilities. The Principal Computer Vision Scientist will be responsible for research direction, model architecture, experimentation, model quality, production readiness, and the practical implementation of computer vision solutions for company products. This is a senior technical leadership position requiring a highly experienced specialist capable of collaborating across research, engineering, product, and production systems. The ideal candidate will evaluate new approaches, design model architectures, conduct experiments, improve model quality, and guide engineering teams in integrating AI / ML capabilities into production workflows. A PhD-level education and extensive hands-on experience in applied Computer Vision, Machine Learning, and Deep Learning are essential. The role demands comfort with business-critical systems, practical production constraints, imperfect datasets, and evolving product requirements. The position will primarily align with the Computer Vision product group but may also support other internal groups requiring expertise in computer vision, image processing, visual search, object detection, segmentation, classification, or model evaluation.

Requirements

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 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.

Nice To Haves

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.

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.
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.