This free Computer Vision Engineer job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring someone to build computer vision systems — detection, classification, segmentation, OCR, or video understanding. The problems and data are specialized, so be concrete about what you're building.
Computer vision candidates want to know the use cases, the data, and whether the role leans toward research, modeling, or shipping production pipelines. Be specific, and mention any real-time or edge constraints.
If the role is broad ML, use the Machine Learning Engineer template; if it's research, use the Research Scientist template.
Writing tips
- Name the CV problems you're solving (detection, segmentation, OCR, video).
- Describe your data and any real-time or edge constraints.
- Clarify the balance of modeling vs. production engineering.
- Distinguish from broad ML and pure research roles.
- Include the salary range and seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. Vision powers [feature/area], and we're hiring a Computer Vision Engineer to build systems that see and understand images and video.
The role
As a Computer Vision Engineer, you'll build systems that interpret visual data — detection, classification, segmentation, or video understanding. You'll train and adapt models, build pipelines, and ship them into production. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Build computer vision systems for [your problems, e.g. detection, OCR].
- Train, fine-tune, and evaluate vision models.
- Build data and inference pipelines for images or video.
- Optimize for accuracy, latency, and any real-time or edge constraints.
- Ship vision features into production and monitor their quality.
What we're looking for
- 3+ years building computer vision or ML systems.
- Strong foundations in computer vision and deep learning.
- Proficiency in [Python] and frameworks like [PyTorch / TensorFlow].
- Experience with image or video data pipelines.
- A pragmatic focus on shipping reliable systems.
Nice to have
- Experience with real-time or on-device inference.
- Background in [your domain, e.g. medical imaging, autonomous systems].
- Publications or open-source work in computer vision.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- Hard vision problems and the data to solve them.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does a Computer Vision Engineer do?
- A Computer Vision Engineer builds systems that interpret images and video — for tasks like object detection, classification, segmentation, OCR, or video understanding. They train and adapt vision models, build data and inference pipelines, and ship vision features into production.
- What's the difference between a Computer Vision Engineer and a Machine Learning Engineer?
- A Computer Vision Engineer specializes in visual data and the models and techniques specific to it. A Machine Learning Engineer is a generalist who builds and deploys models across domains. CV engineers go deeper on image and video problems, pipelines, and constraints like real-time inference.
- What skills should a Computer Vision Engineer have?
- Strong foundations in computer vision and deep learning, proficiency in Python and frameworks like PyTorch or TensorFlow, experience with image or video data pipelines, and a pragmatic focus on shipping reliable systems. Real-time and on-device inference experience is a valuable plus.