Job Description 
Join our AI team to learn and contribute to real-world computer vision systems for automated visual inspection in automotive and manufacturing environments. You'll work alongside experienced engineers on defect detection, quality validation, and edge AI deployment — gaining hands-on exposure to production-grade industrial AI systems. 

What You'll Do: 

• Assist in developing and testing computer vision models for surface defect detection, assembly verification, and anomaly detection under the guidance of senior engineers. 

• Support dataset collection, annotation, cleaning, and augmentation workflows. 

• Help build and evaluate models using object detection (YOLO family), segmentation (SAM/U-Net), and anomaly detection techniques. 

• Run experiments, track results, and document findings clearly (metrics: precision, recall, false rejects/accepts). 

• Assist with model optimization and conversion for edge deployment (ONNX, TensorRT) under supervision. 

• Help analyze model failure cases and contribute ideas for improvement. 

• Participate in code reviews, team discussions, and learn industrial imaging fundamentals (camera setup, lighting, calibration). 

Requirements: 

• Currently pursuing or recently completed a BS in Computer Science, AI, Robotics, Electrical Engineering, or related field. 

• GPA ≥ 3.5/4.0 or 8.5/10 (or equivalent). 

• Strong foundation in linear algebra and probability & statistics (matrix operations, eigenvalues, distributions, Bayesian basics — as applied to ML). 

• Solid foundation in Python and basic understanding of deep learning concepts (CNNs, training/evaluation workflows). 

• Familiarity with at least one deep learning framework (PyTorch or TensorFlow) through coursework, personal projects, or competitions. 

• Basic understanding of computer vision concepts (image processing, object detection, or segmentation) — academic projects or personal portfolio work is a plus. 

• Strong analytical thinking, eagerness to learn, and good communication skills. 

• Comfortable working with Linux and basic command-line tools. 

Nice to Have: 

• Personal or academic projects involving OpenCV, YOLO, or similar CV tools (GitHub portfolio welcome). 

• Exposure to Docker, Git, or cloud/edge platforms (Jetson, Colab, Kaggle). 

• Participation in AI/CV competitions (Kaggle, hackathons, research papers). 

• Experience with C++ is a plus but not required. 

What You'll Gain: 

• Mentorship from experienced AI/CV engineers working on real industrial deployments. 

• Hands-on experience with production-grade datasets and edge AI hardware (NVIDIA Jetson). 

• Exposure to the full ML lifecycle — from data collection to model deployment. 

• Potential for full-time conversion based on performance. 

Duration: Minimum 3 months, 4–6 months preferred (flexible around academic schedule)