The posting
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.
We are looking for an Algorithm Developer to join our Algorithms group and work on a new initiative that turns information from beyond our own sensors into geospatial intelligence. This role is for someone with real algorithmic depth who wants to apply it to a problem where the data is messy, the sources are unreliable, and the answer has to be right.
The work spans the full path from raw, scattered information to structured intelligence anchored in the real world: finding it, judging what matters, making sense of what it contains, and tying it to coordinates. The sources are inconsistent and the material varies widely in quality and format.
Each part is a real algorithmic problem, and none is solved by a single off-the-shelf tool. Knowing which parts suit LLMs and vision-language models, and which are solved far better with geometry, classical computer vision, or a well-chosen heuristic - and being honest about the difference - is the most valuable thing you will bring to this role.
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 can decompose an amorphous problem, identify what is missing, and go get it - talk to domain experts, dig through data, run a quick experiment - and come back with a sharper definition than the one they were handed.
You Will:
- Own problems end to end - from an ambiguous initial ask, through problem definition and prototyping, to a system that runs reliably in production.
- Build systems that gather information from diverse and inconsistent sources and process it reliably at scale.
- Develop methods to judge what is relevant and what is noise, and to measure how much each piece of information can be trusted.
- Anchor information extracted from engineering documents and drawings to real-world coordinate systems.
- Extract structured data from complex, human-oriented documents, combining classical computer vision, document analysis, and modern LLM-based approaches, with deliberate calls about which fits which part of the problem.
- Design evaluation methodologies that measure whether these systems are actually reliable on our data.
- Work directly with mapping experts, surveyors, and algorithm researchers to understand real workflows, and turn that understanding into tools people actually use.
Requirements
- 4+ years of hands-on experience building algorithmic, machine learning, or AI systems that shipped and were used in production.
- B.Sc. in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a similar field.
- A strong algorithmic background is a hard requirement. We are looking for real depth in at least one of: computer vision, data science, NLP, deep learning, document analysis, or geometry and 3D data. This is the part of the profile we are strict about.
- Comfort in a dynamic, loosely defined environment. Problems here arrive amorphous and priorities shift. You should be able to take a vague problem, break it into something tractable, identify what information is missing, and go get it - by talking to domain experts, digging through data, or running a quick experiment.
- Strong proficiency in Python, and solid software engineering practices - clean, maintainable code and robust systems, including data pipelines that handle unreliable inputs.
- Genuine interest in LLMs and modern AI, and a practical understanding of how they work and where they fail. Production experience is not required - a side project, a prototype, or serious personal exploration is enough, as long as you can walk us through what you built and why.
Preferred Qualifications
- Experience with problems that combine text and imagery, such as document analysis, drawings, or diagrams, including OCR, layout analysis, and vision-language models.
- Experience with geospatial data, GIS, CAD, or coordinate reference systems.
- Experience building data collection and processing pipelines over large volumes of unstructured, unreliable data.
- M.Sc. or Ph.D. in a relevant field.
- Familiarity with cloud environment development (AWS).
A note on this role
This is deliberately a broad role, and we know that very few people cover all of it. If you have real algorithmic depth and you are seriously curious about applying it to documents, geometry, and the messy reality of infrastructure - even if you have not worked on all of it before - we would like to hear from you. Tell us what you are strong at and what you want to grow into.
