Exodigo is the leading underground mapping solution for non-intrusive discovery. Our platforms combine multi-sensor fusion, 3D imaging, and AI technologies to create complete, accurate underground maps that enable confident decision-making for customers across the built world. We transform the project lifecycle for our customers, who include key community stakeholders in the utilities, transportation and government sectors.
We are experiencing sky-rocketing growth and closed a historically large $96M Series B round in July of 2025.
Job description
Our Algorithms group is a highly multidisciplinary group at the core of our data processing and detection capabilities, integrating physics, signal processing, computer vision, classical algorithms, and AI models to meet our unique requirements. Our problems are grounded in the physical world: sensor data, engineering drawings, coordinate systems, and the messy reality of infrastructure that was built decades ago and documented inconsistently ever since.
Job Description
We are looking for an AI Algorithms Engineer to join our Computer Vision team and bring LLMs and agentic AI into problems that are anything but generic. This role is for someone who wants to keep their algorithmic craft and add to it — not trade it in. The interesting work here sits exactly where modern AI meets geometry, vision, and physical measurement, and it needs someone who is genuinely strong on both sides of that line.
The work is varied by nature. It can mean finding and making sense of engineering documents that were written by people for people, anchoring what they contain to real-world coordinates, reconciling spatial data from partial and conflicting sources, or building tools and agents inside the professional software our experts work in — platforms such as Civil 3D and ArcGIS. These are separate problems, not stages of one pipeline, and new ones appear regularly.
What ties them together is judgment. Some of these problems are a natural fit for LLMs and agents; others are solved far better with geometry, classical computer vision, or a well-chosen heuristic — and many need both. Knowing which is which, and being honest about it, is the single most valuable thing you will bring to this team.
Our problems also rarely arrive fully specified. You will often start from a one-line description and an unclear success criterion, in an environment where priorities shift. We are looking for someone who is comfortable in that space — who can decompose an amorphous problem, identify what information is missing, and then go get it: talk to domain experts, dig through data, run a quick experiment, and come back with a sharper definition of the problem than the one they were handed.