Senior Computer Vision Engineer

Roles & Responsibilities

• Lead the R&D team's efforts in product development, guiding juniors, and owning the product.
• Design and develop Computer Vision solutions for automated quality inspection and defect detection.
• Develop and train Deep Learning models for:
• Object Detection
• Image Classification
• Semantic / Instance Segmentation
• Visual Anomaly and Defect Detection
• Apply classical image processing techniques using OpenCV or equivalent libraries for preprocessing, feature extraction, measurement, filtering, and post-processing.
• Build datasets, define annotation requirements, perform data augmentation, and evaluate model performance.
• Perform model validation and error analysis focusing on false positives, false negatives, precision, recall, and defect-level performance.
• Develop complete QC pipelines combining model predictions with appropriate business/inspection logic.
• Deploy and optimize Computer Vision models for real-time production environments.
• Troubleshoot real-world QC challenges such as variations in lighting, reflections, camera angles, product positioning, backgrounds, and defect appearance.
• Work with production and quality teams to translate inspection requirements into practical Computer Vision solutions.
• Continuously improve deployed models based on production data and newly identified defect scenarios.

Camera & Vision System Experience

• Experience working with industrial cameras, IP cameras, and video streams.
• Understanding of camera selection and compatibility based on resolution, frame rate, field of view, working distance, and inspection requirements.
• Familiarity with camera calibration, perspective correction, ROI configuration, and image acquisition.
• Understanding of the impact of lighting, lens selection, exposure, motion blur, reflections, and camera positioning on inspection accuracy.
• Experience integrating and validating cameras as part of an end-to-end QC system.

Preferred Experience

• 4+ years of experience.
• Prior experience building and deploying Computer Vision-based QC / automated inspection systems in production.
• Experience detecting manufacturing defects, surface defects, dimensional abnormalities, or product-quality issues.
• Experience with multi-camera vision systems.
• Experience with TensorRT, ONNX, NVIDIA GPUs, Jetson, or similar edge deployment platforms.
• Familiarity with industrial automation, PLC integration, or manufacturing-line systems.
• Experience optimizing Computer Vision pipelines for real-time inference.

Required Skills

• Strong programming experience in Python.
• Strong understanding of Computer Vision and Deep Learning fundamentals.
• Hands-on experience with PyTorch / TensorFlow, OpenCV, or equivalent frameworks.
• Experience with modern object detection and segmentation architectures.
• Understanding the model training, validation, performance metrics, and inference pipelines.
• Experience deploying trained models into production applications.
• Familiarity with GPU-based inference and model optimization.

Good to Have

• Experience with Vision Transformers or newer Computer Vision architectures.
• Exposure to Vision-Language Models (VLMs) for industrial or visual inspection applications.
• Experience with synthetic data generation or handling limited/imbalanced defect datasets.
• Basic understanding of APIs, databases, Docker, and Linux-based deployment environments.