Principal Computer Vision Scientist

Sourceability; United States; Remote; USD 180,000 - 230,000; Full time

Job summary

Sourceability is seeking a Principal Computer Vision Scientist to lead advanced CV and AI/ML efforts within the Global Engineering Organization (GEO). This senior technical leadership role focuses on research direction, model architecture, experimentation, and production readiness for computer vision across company products.

The ideal candidate will have a PhD and hands-on experience in applied CV, ML, and DL, with a track record of building scalable solutions and mentoring engineers and

Qualifications

• PhD in CV/ML with strong publication record.
• 10+ years of CV/ML research and applied ML experience.
• Experience deploying ML models in production and collaborating with product teams.

Responsibilities

• Lead research, architecture, experimentation, and production readiness of CV/ML solutions.
• Define model architecture, strategy, evaluation methodology, and deployment criteria.
• Train, fine-tune, evaluate, optimize, and deploy CV/ML models (detection, segmentation, OCR, visual search).
• Own full model lifecycle: data analysis, labeling requirements, training, tracking, deployment, monitoring, and improvement.
• Build prototypes, proof-of-concepts, and demonstrations to validate new ideas.

Skills

Research leadership

Experiment design

Production ML

Cross-functional collaboration

Education

PhD in Computer Vision / ML

Tools

TensorFlow

PyTorch

OpenCV

MLflow

Job description

Sourceability® is a global digital distributor of electronic components transforming how modern businesses bring products to market. With innovation, quality and logistics as the backbone of the company, Sourceability’s cutting-edge products and services expedite the procurement process across a wide range of industries, including communications/cellular, consumer electronics, and auto manufacturing. 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.