Senior Data Scientist / ML Engineer - Computer VisionPosition OverviewWe are seeking a
Senior Data Scientist / ML Engineer specializing in Computer Vision to support the development, training, optimization, and deployment of machine vision solutions for automated visual inspection in a manufacturing environment.
This role will be a key contributor to an internally developed computer vision platform supporting production packaging operations. Unlike commercial off-the-shelf machine vision solutions provided by external vendors, this system has been custom designed and developed in-house to support specific manufacturing requirements and long-term digital transformation initiatives.
The successful candidate will help scale and mature an existing deployment currently operating on a packaging line equipped with approximately 40 cameras and will contribute to future expansion across additional manufacturing lines and facilities. The position offers the opportunity to work on a highly visible initiative that represents one of the organization's first large-scale implementations of AI-powered visual inspection technology.
This is an excellent opportunity for a hands-on machine learning professional who enjoys solving real-world industrial challenges and helping organizations adopt emerging technologies in operational environments.
Key ResponsibilitiesComputer Vision & Machine Learning - Design, develop, train, validate, and optimize deep learning models for industrial image inspection and anomaly detection.
- Support the full machine learning lifecycle from dataset review through production-ready model evaluation.
- Analyze image datasets and identify opportunities to improve data quality, coverage, and labeling consistency.
- Implement image preprocessing, augmentation, ROI selection, masking, and feature engineering techniques.
- Develop anomaly detection workflows using supervised and unsupervised learning approaches.
- Evaluate model performance and optimize confidence thresholds to minimize false positives and false negatives in production environments.
- Troubleshoot model performance issues and recommend corrective actions.
Operational Technology (OT) Collaboration - Work collaboratively with manufacturing, engineering, automation, and operational technology teams.
- Develop an understanding of manufacturing workflows and production line operations.
- Demonstrate familiarity with Operational Technology (OT) environments and the unique requirements associated with industrial systems.
- Collaborate effectively with stakeholders across both IT and OT functions to ensure successful deployment and adoption of machine vision solutions.
Data & Model Lifecycle Management - Support image dataset development, curation, governance, and traceability.
- Establish robust train, validation, and test methodologies.
- Identify and mitigate data leakage and dataset bias risks.
- Maintain reproducible training procedures and version-controlled model development practices.
- Assist in ongoing performance monitoring and model improvement efforts.
Cloud & Infrastructure Support - Leverage AWS services including SageMaker, S3, CloudWatch, and EC2 to support model development and operational monitoring.
- Work alongside engineering teams to support deployment and maintenance of machine learning workloads.
Documentation & Knowledge Transfer - Create and maintain detailed technical documentation covering datasets, model configurations, training runs, validation results, and deployment procedures.
- Document model decision-making processes and provide traceability for future audits and troubleshooting.
- Support ongoing knowledge transfer and cross-functional training activities.
Recommended ExperienceRequired - 12+ years of professional experience in Data Science, Machine Learning, Artificial Intelligence, or related fields.
- 3+ years of hands-on experience developing Computer Vision and Deep Learning solutions.
- Proven experience delivering image-based machine learning solutions through the complete lifecycle:
- Dataset assessment
- Data preparation
- Model training
- Hyperparameter tuning
- Model evaluation
- Performance optimization
- Experience troubleshooting machine learning models and interpreting training, validation, and inference results.
- Experience working directly with business and technical stakeholders to solve operational challenges.
Preferred - Experience with industrial machine vision or automated visual inspection systems.
- Experience developing anomaly detection solutions.
- Familiarity with manufacturing, pharmaceutical, life sciences, or regulated production environments.
- Familiarity with Operational Technology (OT) environments and industrial systems.
- Understanding of GxP-controlled environments and validation processes.
Not Required - Prior SmartVision experience.
- SCADA programming experience.
- PLC programming experience.
- AWS administration experience.
Structured onboarding, shadowing, and knowledge transfer will be provided to support ramp-up.
Required Technical SkillsProgramming & AI/ML - Strong Python development skills.
- Strong experience with PyTorch or equivalent deep learning frameworks.
- Experience utilizing modern AI-assisted development tools, including GitHub Copilot, Codex-based development workflows, or similar code-generation and productivity platforms.
- Ability to rapidly prototype, test, and iterate on machine learning solutions using AI-enhanced development practices.
Computer Vision - Image classification
- Object detection
- Segmentation
- Feature extraction
- Visual anomaly detection
- Image preprocessing and augmentation
- ROI selection and masking
Model Development - Hyperparameter tuning
- Threshold optimization
- Sensitivity analysis
- Model explainability and troubleshooting
- Model evaluation using:
- Precision
- Recall
- F1 Score
- Confusion matrices
- False Positive Analysis
- False Negative Analysis
AWS Knowledge - Amazon SageMaker
- Amazon S3
- Amazon CloudWatch
- EC2 familiarity preferred
Soft Skills: - Strong accountability and ownership of deliverables.
- Excellent communication skills with both technical and non-technical audiences.
- Patience and adaptability while working through evolving datasets, new technologies, and operational constraints.
- High availability and responsiveness when supporting business-critical initiatives.
- Strong collaboration and relationship-building skills.
- Curiosity and willingness to learn a custom-developed platform and manufacturing processes.
- Ability to work independently while maintaining alignment with project stakeholders.
Work Location - Remote work arrangement is acceptable.
- Periodic travel to manufacturing facilities and project locations should be expected for onboarding, shadowing, knowledge transfer, testing, and deployment support.
The estimated pay range for this position is USD $75.00/Hr - USD $80.00/Hr. Exact compensation and offers of employment are dependent on job-related knowledge, skills, experience, licenses or certifications, and location. We also offer comprehensive benefits. The Talent Acquisition Partner can share more details about compensation or benefits for the role during the interview process.
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