Hiring nowComputer vision and spatial perception

Computer Vision Engineer

Also called Vision ML Engineer

A Computer Vision Engineer builds detection and recognition systems that work in a defined setting, and proves how well they work. Companies rely on them to turn images into dependable information a product can act on.

What they do

Most days go on the unglamorous parts of vision work. They look at images and failure cases, decide what the system must detect or segment, and build the pipeline around that. They train and tune deep learning models in PyTorch, use OpenCV for image handling, and design experiments that show whether a change helped. They break results down by scene, object class and operating condition. They also build the interface that lets other engineers or customers run the model, and they keep checking it against new conditions.

What a strong hire delivers

In the first months, a strong hire delivers a validated vision model for one defined setting, plus a deployment interface the team can use. They leave behind an evaluation set and a clear report of error by scene, object class and operating condition. The team knows where the model is reliable, where it is not, and what to improve next.

How you can tell they are good

Ask them to walk through a vision system they shipped. Strong people talk about the data and the failure cases before the architecture. They can show error broken down by condition, explain how they chose the test set, and say how confident they are in each number. Be wary of a single headline accuracy figure with no breakdown.

Why it is hard to fill

The title is used loosely. Many candidates have trained models on tidy benchmarks but never validated one in a messy real setting. Others come from image processing or robotics and are lighter on deep learning. Few are strong at detection, segmentation, honest evaluation and deployment together. That mix is what we are asked for, and it is scarce.

Where to find them

Look at applied ML engineers, image processing specialists and computer vision researchers who have moved into product work. They come from robotics, inspection and industrial automation, medical imaging, mapping, retail and security technology, and from AI start-ups with a camera or sensor at the centre. Strong candidates often have a research-style grounding in experimental design.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Computer Vision Engineer

  1. Tell me about a vision model you took from first experiment to deployment. What changed between the two?

    Strong answers cover data gaps, failure cases found late, and changes to the pipeline or evaluation. They show ownership beyond training. Worry if the story stops at a benchmark score or they cannot say what broke in real use.

  2. How do you report model error so a non-specialist can decide whether to ship?

    Good answers break error down by scene, object class and operating condition, and state uncertainty plainly. They link errors to consequences for the product. Worry about a single accuracy number or vague reassurance.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring a Computer Vision Engineer to build detection and recognition systems for a defined setting. You will own the work from first experiment to a validated model with a deployment interface, and show clearly how well it performs in each condition we care about.

What you will do

  • Build detection and segmentation pipelines for our defined use case
  • Train, tune and compare deep learning models in PyTorch
  • Design experiments and evaluation sets that reflect real operating conditions
  • Report error by scene, object class and operating condition
  • Quantify and communicate model uncertainty
  • Build and maintain the interface used to deploy the model
  • Investigate failures and decide what to fix first

What you will bring

  • Vision systems you have built and shipped, with results you can explain
  • Strong working knowledge of deep learning for detection and segmentation
  • Experience with PyTorch and OpenCV
  • Sound experimental design and evaluation habits
  • Clear written and spoken reporting of results to non-specialists
  • Evidence of handling messy real-world image data

Nice to have

  • Experience with uncertainty quantification or calibration
  • Background in image processing or robotics
  • Experience deploying models to constrained hardware

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Computer visionAdvancedAnalyse or generate visual information
  • Deep learningAdvancedBuild and diagnose trainable neural models
  • Experimental designWorkingChoose comparisons that identify credible effects
  • Uncertainty quantificationWorkingEstimate confidence, calibration and model uncertainty

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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