Principal Computer Vision Scientist

Principal Computer Vision Scientist

About the Role:

We are looking for a Principal Computer Vision Scientist to lead advanced Computer Vision and AI / ML work inside GEO. This role will own research direction, model architecture, experimentation, model quality, production readiness, and practical implementation of computer vision solutions used in company products.

This is a principal-level technical leadership role for a highly experienced specialist who can work across research, engineering, product, data, and production systems. The role does not own formal people management, but it is expected to provide technical direction, mentor engineers, establish standards, and help teams bring production-quality AI / ML capabilities into real business workflows.

The right candidate should be comfortable working with business-critical systems, imperfect datasets, evolving product requirements, and practical constraints involving latency, scalability, reliability, maintainability, and user acceptance.

Computer Vision / AI Focus:

The primary focus of this role is the Computer Vision product group inside GEO. Depending on business priorities, the role may support mobile image-capture workflows, AI-assisted product features, data and annotation pipelines, visual search, object detection, segmentation, classification, OCR, image understanding, model evaluation, and production ML systems.

Insight on Your Impact:

• Lead the 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, 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, mobile workflows, APIs, and services.
• Define standards for model evaluation, versioning, dataset management, reproducibility, documentation, and MLOps practices.
• Evaluate research papers, open-source models, AI platforms, and emerging 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 by clarifying technical complexity, dependencies, risks, and realistic delivery assumptions.
• Create model evaluation reports, architecture notes, technical documentation, and recommendations for engineering and product teams.
• Partner with Product / Delivery Managers, Engineering Managers, Team Leads / Architects, QA, DevOps, Data, and business stakeholders to ensure AI / ML work is practical, production-ready, and supportable.
• Operate within agreed product priorities and delivery commitments; formal people management, business prioritization, UAT ownership, and independent delivery-date commitments remain with the appropriate Engineering and Product leaders.

Your Qualifications, Your Influence:

• PhD in Computer Science, Computer Vision, Machine Learning, Artificial Intelligence, Applied Mathematics, Electrical Engineering, Robotics, or a closely related technical field.
• 7+ years of hands-on Machine Learning / Deep Learning experience with a strong focus on Computer Vision.
• Strong practical experience with PyTorch and / or TensorFlow and strong Python development skills.
• Experience with OpenCV, NumPy, Pandas, scikit-learn, and the 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, OCR, 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 and optimizing 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, validation datasets, and model error analysis.
• Ability to read, understand, and evaluate technical documentation and research papers in English.
• Ability to explain complex technical topics, limitations, risks, and tradeoffs to engineering, product, and business stakeholders.
• Experience working in an Agile software development environment and in distributed teams across multiple locations and time zones.
• Strong ownership mindset, good judgment, and ability to make practical technical decisions under uncertainty.

Preferred Skills and Technical Familiarity:

• Post-PhD research or industry experience in applied Computer Vision.
• Publications, patents, or a strong applied research record in Computer Vision, Machine Learning, or AI.
• Experience leading technical direction for AI / ML projects and 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, 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.

Success in the First 90 Days

• Understand the relevant product areas, users, business workflows, image-capture workflows, datasets, model use cases, and current technical risks.
• Establish working relationships with Engineering Managers, Team Leads / Architects, Product / Delivery Managers, engineers, QA, DevOps, Data, and business stakeholders.
• Review current Computer Vision and AI / ML work, including models, datasets, evaluation methods, annotation processes, production integration, and known quality issues.
• Identify the most important model-quality risks, data-quality gaps, technical-debt items, production risks, and maintainability concerns.
• Define or improve model-evaluation criteria, validation processes, dataset requirements, reproducibility standards, and model-readiness expectations.
• Improve technical clarity of the active AI / ML backlog by adding design notes, technical breakdowns, dependencies, estimates, and risks.
• Lead at least one meaningful model improvement, prototype, evaluation effort, or production risk-reduction activity.
• Improve documentation around model architecture, data flows, evaluation results, known limitations, and production behavior.
• Create an initial technical roadmap or remediation plan for Computer Vision work aligned with product priorities and engineering capacity.
• Help onboard or mentor engineers working on Computer Vision, AI / ML, or related product features.