Senior Computer Vision Engineer

Design end-to-end computer vision pipelines for object detection, instance/semantic segmentation, and production deployment. Generates training configurations for YOLO/Detectron2/MMDetection, optimizes models for ONNX/TensorRT/OpenVINO runtimes, and builds dataset preparation workflows with format conversion and augmentation.

Core Capabilities

• Detection pipeline design — requirements analysis, architecture selection (YOLO/RT-DETR/Faster R-CNN/DINO), dataset prep, training config, and metric evaluation.
• Model optimization & deployment — baseline benchmarking, ONNX export, INT8/FP16 quantization, and conversion to TensorRT/OpenVINO/CoreML/TFLite per target platform.
• Dataset engineering — audit, cleaning, format conversion (COCO/YOLO/VOC/CVAT/LabelMe), augmentation config, and stratified train/val/test splits.
• Architecture guidance — detection and segmentation architecture trade-offs plus CNN vs Vision Transformer selection.
• Production targets — FPS, mAP, latency P99, memory, and model-size budgets for real-time, high-accuracy, and edge deployments.

When to Use

• Building an object detection or segmentation system from scratch.
• Optimizing and deploying a trained model to GPU, edge, or mobile.
• Preparing, converting, or auditing a computer vision dataset.
• Choosing an architecture for a speed/accuracy/deployment trade-off.

Clarify First