Computer vision engineers ship models that run on cameras, drones, robots, factory lines, and phones - which means your resume must answer two questions a software-only ML resume doesn't: what's the accuracy on the actual task (mAP, IoU, Dice, top-1) and can it run at the required frame rate on the target hardware? Hiring managers in autonomous, robotics, AR/VR, and medical imaging filter aggressively for both.
The strongest CV resumes pair a benchmark improvement with a deployment constraint that was met - 'mAP@50 0.78 → 0.86 while staying under 12 ms on Jetson Orin'. Reference the architectures that matter for your sub-area (YOLO/DETR for detection, SAM/Mask R-CNN for segmentation, ViT/CLIP for representations, NeRF/Gaussian splatting for 3D), the optimisation toolchain (TensorRT, ONNX, OpenVINO, CoreML), and the data-engineering reality of CV (annotation pipelines, active learning, hard-negative mining).
ATS keywords: computer vision, deep learning, PyTorch, OpenCV, YOLO, object detection, segmentation, CUDA, TensorRT, ONNX, edge AI, Python, C++, image processing.
This example works for
Computer Vision Engineer
CV Engineer
Perception Engineer
ML Engineer (Vision)
Image Processing Engineer
What you get with every LumiCV resume
- ATS-parseable PDF. Real text, no images - and parse-tested, so every field is recovered in the correct reading order.
- AI resume tailoring. Paste the job description. LumiCV rewrites your bullets to match the role's exact keywords.
- Match score and keyword gap analysis. See exactly where you match the role and where you're missing before you apply.
- 28 templates, 14 free. Every country format is free. Switch layouts in one click; your content stays.
