Hiring nowComputer vision and spatial perception

3D Vision Researcher

Also called Spatial AI Researcher

A 3D Vision Researcher recovers spatial structure from images and depth data, then proves it holds up. Companies building robotics, mapping, AR or spatial products rely on this work to make machines understand the physical world reliably.

What they do

They build and test models that turn images, video or depth readings into 3D structure. That might be depth estimation, multi-view reconstruction, or learned scene representations. Most days involve reading recent papers, reproducing a baseline in PyTorch, using OpenCV for calibration and geometry checks, and running experiments on messy real captures. They design evaluation sets, inspect failure cases, and work out whether errors come from the data, the model or the camera setup. They also write up findings so engineers can decide what to ship.

What a strong hire delivers

In the first months, a strong hire delivers a trusted evaluation setup for 3D reconstruction, with clear geometric error measures and a held-out set of scenes the model has never seen. They produce a working baseline, a ranked list of failure modes, and one or two model improvements that are backed by evidence. They also leave the team with clearer decisions about data capture and sensor choices.

How you can tell they are good

Ask them to walk through a reconstruction or depth project end to end. Good candidates explain the geometry, not just the network, and can say how they measured accuracy and what broke on unfamiliar scenes. Ask how they handled scale ambiguity, calibration error or reflective surfaces. Be wary of anyone who only quotes benchmark scores or cannot describe a failure case.

Why it is hard to fill

The title is used loosely. Spatial AI, 3D vision, SLAM, neural rendering and photogrammetry all overlap, and candidates often know one well and the others thinly. Many strong people are classical geometry experts who have moved into deep learning, or ML researchers who have never dealt with real sensor noise. The mix of both is scarce, and most are already settled in research teams or robotics groups.

Where to find them

Look for computer vision researchers, robotics perception engineers, SLAM and mapping engineers, and photogrammetry specialists who have moved into learning-based methods. They sit in university labs, frontier labs, robotics and autonomy companies, AR and VR teams, mapping businesses and graphics groups. Applied ML people with image processing backgrounds can also grow into the work if they have a strong grounding in geometry.

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 3D Vision Researcher

  1. Describe a 3D reconstruction or depth estimation project you led. What was the hardest failure you found?

    A strong answer names the task, data and metric, then a specific failure such as textureless surfaces or scale drift, and how they diagnosed it. Worry if the story is all architecture and no measurement or debugging.

  2. How would you evaluate whether a reconstruction model generalises to scenes it has not seen?

    Look for held-out scenes that differ in lighting, layout and sensor, geometric error measures beyond a single score, and per-scene breakdowns. Worry if they test on near-duplicates of training data or trust one aggregate number.

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

Job description you can post

We are hiring a 3D Vision Researcher to help our products understand the physical world. You will recover spatial structure from images and depth data, build ways to measure how well it works, and turn research into methods our engineers can ship.

What you will do

  • Develop models that recover 3D structure from images, video or depth data
  • Build evaluation sets and geometric accuracy measures for reconstruction
  • Test robustness on scenes the models have not seen
  • Analyse failure cases and trace them to data, model or sensor causes
  • Estimate and report uncertainty in 3D outputs
  • Work with engineers to move successful methods into products
  • Write clear summaries of experiments and decisions

What you will bring

  • Demonstrated work in reconstruction, depth estimation or related 3D vision
  • Strong grounding in multi-view geometry and camera calibration
  • Hands-on deep learning experience in PyTorch
  • Practical use of OpenCV or similar for image and geometry tasks
  • Careful experimental design, with evidence of rigorous evaluation
  • Ability to explain technical trade-offs to non-specialists

Nice to have

  • Experience with neural scene representations
  • Experience working with real sensor data such as depth cameras or lidar
  • Published or open-source work in 3D vision

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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