About the Role
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.
Our customers are the utilities, transportation agencies, and government bodies that build the world's infrastructure — LA Metro, Caltrans, National Grid, Israel Railways, and 100+ teams worldwide. To date we've de-risked $75B in infrastructure investment and mapped 86.1M square feet of ground.
We are experiencing sky-rocketing growth and closed a historically large $96M Series B round in July of 2025.
Job description:
Our Algorithms Team is a highly multidisciplinary group spanning physics, signal processing, computer vision, classical machine learning, and modern AI models, built around problems that don't have off-the-shelf solutions.
As an Algorithm Engineer on Exodigo's Algorithms Team, you'll build the algorithms that turn raw subsurface signals into an accurate map — automating and outperforming work that today only geophysicists and civil engineering experts can do. You'll work across the full span of our data: multi-sensor field acquisitions, and the existing record of what's already known underground. The hard part is the fusion — reconciling physics-driven detections with incomplete and contradictory records into a single coherent picture of the subsurface. You'll work directly with our mapping and engineering experts to validate what you build, and with our software team to get it into the production pipeline.
Responsibilities
Translate real-world physical and engineering problems into mathematical models and algorithmic solutions.
Design algorithms that fuse heterogeneous sources — sensor modalities with different physics and resolution, alongside vector, raster, tabular, and unstructured data.
Extract structure from noisy signal: detection, segmentation, and interpretation where ground truth is scarce and the physics is only partly cooperative.
Build evaluation methodology alongside the algorithms, so downstream engineers know what to trust and how much.
Partner with the maps analysis team to test, benchmark, and iterate on performance.
Requirements:
B.Sc. in technical field such as Electrical Engineering, Computer Engineering, Computer Science or similar.
5+ years of relevant industry experience.
Solid grounding in machine learning, with judgment about when a classical method beats a deep model.
Expertise in graph theory, computational geometry, statistics, optimization, and data analysis.
Strong Python programming skills (Pandas, NumPy, SciPy, PyTorch, etc.).
Versatility in selecting and combining tools to fit the problem at hand.
Nice to Have
M.Sc. in relevant field.
Experience in Geo Information Systems (GIS).
Experience working with BIM data and 3D modelling.
Experience applying LLMs to information extraction from unstructured or scanned documents.
Experience in cloud environment development (AWS).