Post a Computer Vision Engineer job.
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Click "Post a job — it's free" — no subscription or credit card required. Describe the computer vision problem precisely: object detection, image segmentation, OCR, anomaly detection, 3D reconstruction, or real-time video analysis. Specify the deployment environment — cloud inference, edge hardware, or embedded systems — and name the tools in your stack (PyTorch, OpenCV, TensorRT, YOLO). Domain context matters enormously in CV: a medical imaging role and a retail analytics role require fundamentally different engineering experience. The more specific your post, the faster you will attract engineers who have actually solved the same category of problem.

The talent section on this page is pre-filtered for computer vision and image AI specialists. Browse profiles showing headline, core tools, and background. Click any profile to review their CV projects in detail: the visual task they solved, the architecture they used, the dataset they worked with, the evaluation protocol, and the deployment environment. For CV roles, prioritize profiles that match your specific domain (medical, automotive, retail, industrial) and deployment target (cloud vs. edge). Domain mismatch is the most common and costly mistake in computer vision hiring — a general CV engineer from one domain rarely transfers cleanly to another without significant ramp-up.

PyTorch Vision, TensorFlow, OpenCV, YOLO (v5/v8/v9), Detectron2, MMDetection, Segment Anything (SAM), image classification, object detection, instance segmentation, semantic segmentation, depth estimation, optical flow, 3D point cloud processing, video understanding, OCR (Tesseract, PaddleOCR, TrOCR), anomaly detection, and multimodal vision-language models (CLIP, LLaVA). On the deployment side: TensorRT, ONNX, Core ML, OpenVINO, and edge AI frameworks for NVIDIA Jetson, Raspberry Pi, and custom hardware. Specify domain (medical, retail, autonomous systems) — it is the most effective way to filter for engineers with directly applicable experience.

Yes — completely free, no conditions. Post your CV role today without entering a credit card or selecting a plan. There are no per-post fees, no hiring placement charges, and no feature limits for employer accounts. RemoteAI is free for companies in full: posting, browsing CV engineer profiles, messaging candidates, and managing your pipeline are all included. You can post multiple CV roles simultaneously. The platform is built around AI talent — companies are never charged at any stage.

Most computer vision job posts receive their first applications within 24–48 hours. CV engineers with production experience in specific domains (medical imaging, autonomous systems, retail analytics) are actively looking and respond quickly to well-described roles. As with all technical specializations on RemoteAI, specificity is the key lever: a post that names the visual task, the deployment target, and the domain will consistently outperform a generic "computer vision engineer" listing. If your role involves edge deployment or a specialized domain like pathology imaging, say so explicitly — those details attract exactly the engineers who are qualified.

Yes. Every profile is reviewed before being listed on RemoteAI. You see a genuine headline, descriptions of the CV systems built and deployed, the datasets used (including scale and domain), the evaluation protocols applied, and the production environments involved. Profiles that describe real visual AI systems — the task, the architecture choice, the deployment constraints, and the outcome — are the ones that have been through actual engineering work. Vetting removes noise. Your technical interview should validate domain fit, evaluation rigor, and experience with the specific deployment environment your system requires.

Yes. RemoteAI has computer vision engineers specializing in edge AI deployment: NVIDIA Jetson (Nano, Xavier, Orin), Raspberry Pi, Intel OpenVINO, Qualcomm AI platforms, and custom embedded hardware. Edge CV is a distinct specialization requiring expertise in model quantization (INT8, FP16), pruning, TensorRT optimization, ONNX runtime, and hardware-aware architecture design. If your use case requires real-time inference on constrained hardware, specify the target device and latency requirement in your post. Engineers with relevant hardware experience will self-select, which dramatically reduces the interview overhead of screening out candidates with only cloud-inference backgrounds.

The most effective CV job posts describe five things: the visual task (what the model sees and what it must output), the domain (medical, retail, manufacturing, automotive), the deployment environment (cloud API, edge device, real-time video stream), the current state of the system (greenfield vs. improving an existing model), and the evaluation bar (what performance is acceptable vs. production-ready). For example: "building a defect detection system for circuit boards — YOLOv8 base, running on NVIDIA Jetson Orin, 95% recall required at 30fps" tells a qualified engineer everything they need to know to decide whether to apply. That specificity is worth more than any recruiter keyword list.

Free plan: create a company account and applications stay inside RemoteAI — dashboard, messaging, AI matching, no email or external link involved. The $199 option: no account, no dashboard — candidates apply straight to your own email or link instead, and your listing is reviewed and live within one business day.