Lead Graph Optimization Scientist — Trace & Lane Mapping

Tomtom-7dbebcc; Amsterdam; Hybrid; EUR 65,000 - 95,000; Full time

Benefits offered by this job

Competitive compensation

Personal development budget

Paid learning days

Parental leave

Work flexibility

Home office setup budget

Global mobility

Job summary

TomTom is seeking a Software Engineer to advance the lane-level road graph from crowd-sourced traces. You will design scalable algorithms for map-matching, clustering, and drift correction, and build the lane graph using optimization approaches at production scale.

You will combine deterministic methods with modern ML, work with Azure and Databricks, and deploy Docker-based solutions to support continental map scopes. Collaboration across teams is essential for robust map-making pipelines.

Qualifications

• Master's degree in computer science, robotics, applied mathematics, Engineering, or a related field.
• Software Engineer with at least 3-4 years of professional experience.
• Strong Python skills; C++ experience for performance-critical code is a plus.
• 3-4 years of hands-on experience with optimization-based estimation: linear and non-linear programming, graph optimization, and probabilistic state estimation.
• Demonstrated experience processing large-scale vehicle trace or trajectory data: map-matching, trace alignment and registration, clustering, and sensor fusion.
• SLAM and mapping knowledge with real sensor data.
• Working knowledge of ML applied to geometric/graph problems (GNNs, learned lane/topology detection).
• Experience with Azure and Databricks.
• Proficient in deploying applications using Docker containers.
• Ability to think end-to-end and deliver high-quality solutions.

Responsibilities

• Design and implement scalable algorithms for map-matching, trace clustering, and drift correction at fleet scale.
• Build and optimize the lane graph as optimization problems (LP/ILP, graph-based) at production scale.
• Combine deterministic methods with learned models (ML detectors, GNNs, transformers) where beneficial.
• Architect high-performance implementations robust for production-ready deployment across continental map scopes.
• Collaborate with cross-functional teams to integrate trace-processing and lane-graph models into production pipelines.
• Stay current with advances in trace-based mapping, SLAM, and geospatial deep learning.
• Deploy solutions using Docker containers on cloud platforms such as Azure.

Skills

Graph optimization

LP/ILP

SLAM/Mapping

Python

C++

Optimization-based estimation

Azure

Databricks

Docker deployment

Trace processing

Education

Master's degree in CS/Robotics/Applied Math/Engineering

Tools

Azure

Databricks

Job description

TomTom is seeking a Software Engineer to advance the lane-level road graph from crowd-sourced traces. You will design scalable algorithms for map-matching, clustering, and drift correction, and build the lane graph using optimization approaches at production scale.

You will combine deterministic methods with modern ML, work with Azure and Databricks, and deploy Docker-based solutions to support continental map scopes. Collaboration across teams is essential for robust map-making pipelines.