Post a Computer Vision Engineer job.
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Reach computer vision engineers and image AI specialists among 3,000+ AI professionals on RemoteAI.
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How it works
Post a job, read applications and hire in one place.
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Computer vision engineers on RemoteAI
Computer vision engineers among 3,000+ AI professionals. Post a job to reach them.
Lahiru Nuwan
Aleksey
Hongxi
Samuel Alejandro
Hiring context
Computer vision hiring is defined by domain, not just skill
Computer vision is one of the most domain-specific disciplines in AI. An engineer who builds defect detection for semiconductor manufacturing has completely different instincts and methods from one who builds person re-identification for retail analytics. The visual patterns, annotation requirements, model architectures, and production edge cases in each domain are so different that cross-domain transfer is often much slower than it looks in an interview. Domain match is not a preference in CV hiring — it's frequently the difference between a productive hire and a costly ramp-up.
The deployment environment is another dimension that must be defined upfront. Cloud-based CV inference and real-time edge AI for embedded hardware are completely different engineering disciplines. Edge deployment requires model quantization, hardware-aware optimization, and real-time performance engineering — skills most cloud CV engineers have never needed. RemoteAI's computer vision profiles describe both the visual task and the deployment target. You get the domain and environment specificity you need to identify genuine fit before the first interview — not three months into onboarding.
Why RemoteAI
Built for computer vision hiring.
Not general recruiting.
Generic job boards attract anyone. RemoteAI attracts the CV engineers who build real vision products.
AI-only talent pool
Every profile on RemoteAI is an AI specialist. No noise, no irrelevant applications from unrelated fields.
Reviewed new profiles
Every new profile is reviewed by our team. You see real names, real descriptions and the skills each person lists.
Applications in your dashboard
Talents apply on the site. You read each application, message people and keep track of them in your dashboard.
One free job a month
Post one job a month for free. It stays open 14 days and is reviewed first if your company website is not verified. Paid plans cover more jobs.
Global remote talent
Access AI engineers from India, the US, UK, Canada, Germany and many more countries — all remote-ready.
Full hiring workflow
Review applications, message candidates, schedule interviews, and track progress — all in one platform.
Step by step
How to post a Computer Vision job on RemoteAI
Name the visual task, the deployment target (cloud, edge, mobile) and the domain, and add a pay range. Edge and real-time inference work is a specialty of its own, so say so if you need it.
Describe the vision problem precisely
Object detection, image segmentation, OCR, 3D reconstruction, or video analysis? Computer vision is broad. Specificity attracts the right specialists and prevents expensive mismatches.
Specify the deployment environment
Cloud inference, real-time edge, embedded systems? Edge deployment requires entirely different optimization skills than cloud API work. Be explicit about hardware constraints.
Publish and receive applications
Talents apply on the site and you read applications in your dashboard, with no recruiter and no agency fee.
Hiring guide
What to look for when hiring for Computer Vision
Not all candidates are equal. These are the signals that separate engineers who deliver in production from those who only shine in interviews.
Domain match is non-negotiable
Medical imaging, autonomous vehicles, retail analytics, and industrial inspection are separate skill sets. Do not assume CV skills transfer across domains — verify explicitly.
Framework and toolchain depth
PyTorch Vision, OpenCV, TensorRT, ONNX, YOLO. Verify which tools match your production environment before starting technical interviews.
Data annotation experience
Great CV engineers understand labeling quality, annotation guidelines, and dataset curation — not just model training on pre-cleaned benchmarks.
Edge deployment skills
If on-device inference is required, explicitly verify TensorRT, Core ML, or ONNX optimization experience. This is a distinct specialization.
Model evaluation rigor
mAP, IoU, precision-recall curves — and crucially, whether they define evaluation protocols before training, not after. Retroactive evaluation is a red flag.
Real-world robustness experience
Lighting variation, occlusion, blur, domain shift — production CV fails at the edges. Ask for their hardest debugging story and what they learned.
What you can post
Every AI specialty. One platform.
AI & ML Engineering
Model development, production ML & applied AI delivery
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LLM products, RAG systems & prompt design
MLOps & Infrastructure
Deployment, observability & scalable AI systems
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Roadmap, adoption & AI product execution
Automation & AI Workflows
Agents, copilots, orchestration & process design
Data Science & Analytics
Forecasting, experimentation & decision support
FAQ
Questions from companies hiring computer vision engineers
Everything you need to know before posting your first AI job.
Click "Post a job". You can post one job a month for free: it stays open 14 days and is reviewed first if your company website is not verified. Paid plans cover more jobs. 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, one job a month is free, with no credit card. It stays open 14 days and is reviewed first if your company website is not verified. Paid plans cover more jobs, and you can also post a single job for $99 without an account. There is no placement fee when you hire.
It depends on the role and on how clear the post is, so we do not promise a delay. CV engineers with production experience in a specific domain (medical imaging, autonomous systems, retail analytics) recognise a well-described role quickly. 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. New profiles are 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. Profile review 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: you create a company account and read applications in your RemoteAI dashboard, with messaging and AI matching. One job a month, open 14 days, reviewed first if your website is not verified. The $99 option: no account and no dashboard. Candidates apply to your own email or link, and the listing is reviewed before it goes live.
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One job a month is free (open 14 days, reviewed first if your website is not verified), or $99 for one job without an account. No agency fees, no placement charges.