Senior Applied Scientist - Graph Optimization & Trace Alignment

The Road Features Group, a sub-organization within ADAS & ADS at TomTom, is the algorithmic engine that drives the creation of highly accurate HD maps to facilitate lane level navigation of Autonomous Vehicles. We create digital twins of road networks faster and more accurately than ever before. Our largest and freshest signal is crowd-sourced: millions of kilometers of vehicle traces and sensor observations collected from production fleets every day, alongside aerial and street-side imagery. Turning that noisy, massive stream into a precise lane graph is the core algorithmic challenge of the group. The Road Surface Graph (RSG) & Lanes team is pivotal in this effort, extracting drivable surfaces and lane centerlines at continental scale. Join us in setting new standards for mapping technology and making road feature extraction smarter and more efficient. We are looking for a Software Engineer with deep experience in graph optimization, linear programming, and trace-based mapping (SLAM-style estimation) who can take large volumes of crowd-sourced vehicle trace data and turn it into a lane-level road graph. You will design the algorithms that align, filter, cluster, and fuse millions of noisy GPS and sensor traces, and that construct and optimize the resulting lane graph: combining traditional deterministic methods (linear and non-linear programming, factor graphs, combinatorial optimization on road networks) with modern AI/ML approaches where they beat the classical baseline. This is a role for someone who thinks in graphs and estimators first and treats both optimization solvers and learned models as tools in the same toolbox.