Job Description Develop, test, and deploy reliable AI models for automated visual inspection in automotive and manufacturing environments—covering defect detection, quality validation, and real-time decision-making under industrial constraints. 

Key Responsibilities: 

• Develop, test, and deploy advanced computer vision and deep learning models tailored for industrial inspection tasks, including surface defect detection (scratches, dents, cracks), assembly verification, and anomaly detection. 

• Develop, test, and deploy robust AI solutions capable of operating reliably under real-world manufacturing conditions such as varying lighting, noise, occlusions, and product variability. 

• Collaborate with hardware and automation teams to define camera systems, lighting setups, sensor configurations, and data acquisition strategies for optimal inspection performance. 

• Collaborate with cross-functional teams to design and implement data pipelines, including dataset collection, annotation workflows, versioning, and validation frameworks with clearly defined metrics (precision, recall, false rejects/accepts). 

• Deliver production-grade AI systems optimized for edge deployment with strict latency, stability, and accuracy requirements. 

• Contribute to continuous improvement of inspection performance through failure analysis, model retraining, and feedback loops from production environments. 

Required Qualifications: 

• BS+ in Computer Science, AI, Robotics, Electrical Engineering, or related field. 

• Strong Python; experience with deep learning frameworks (PyTorch preferred, or TensorFlow/JAX). 

• Solid computer vision foundation: image processing, object detection (YOLOv8/v9/v10/v11, RT-DETR, DETR-family), segmentation (Segment Anything Model (SAM/SAM2), U-Net, Mask R-CNN), anomaly detection techniques (PatchCore, EfficientAD, PaDiM, FastFlow). 

• Experience handling industrial imaging data: cameras (area/line scan), optics, lighting, calibration, and image preprocessing. 

• Experience building reliable ML systems: dataset curation, data augmentation, model validation, and performance optimization. 

• Familiarity with deploying models in production environments (Linux, Docker, REST/gRPC APIs). 

• Strong debugging and analytical skills for diagnosing model failures and improving robustness. 

Preferred Qualifications: 

• Experience in automotive or manufacturing quality inspection systems. 

• Exposure to edge AI deployment and model optimization (NVIDIA Jetson Orin, TensorRT, ONNX Runtime, OpenVINO, model quantization/pruning, knowledge distillation). 

• Exposure to vision-language and foundation models for inspection (e.g. Grounding DINO, CLIP-based approaches for few-shot defect classes). 

• Experience with synthetic data generation, diffusion models/GANs, or simulation (e.g. NVIDIA Omniverse/Isaac Sim/Blender) for defect modeling. 

• Understanding of quality standards (Six Sigma, SPC, zero-defect manufacturing concepts). 

• Experience with 3D vision, multi-camera systems, point cloud processing, or multimodal inspection. 

• Familiarity with MLOps tooling for experiment tracking and model versioning (MLflow, Weights & Biases, DVC). 

Tools & Stack 

• Python, PyTorch / TensorFlow, OpenCV 

• ONNX, ONNX Runtime, TensorRT, OpenVINO 

• Industrial cameras (Basler, Cognex, Keyence, etc.), lighting systems 

• Docker, Linux, REST/gRPC APIs 

• Git, CI/CD pipelines 

• MLOps: MLflow / Weights & Biases (optional) 

• Edge AI platforms: NVIDIA Jetson Orin, IPC (optional)