Senior Applied Scientist: Graph Optimization for HD Maps

TomTom International BV; Amsterdam; Hybrid; EUR 75,000 - 120,000; Full time

Benefits offered by this job

Personal development budget

Paid learning days

Parental leave

Job summary

TomTom is seeking a Software Engineer to design and implement scalable graph-based lane-graph extraction and optimization algorithms. You will transform crowd-sourced traces into a coherent lane graph and integrate both deterministic and learned methods.

The role emphasizes production-grade performance and cross-functional collaboration across ADAS & ADS teams. You will work with Python and C++, apply SLAM-aware mapping techniques, and deploy solutions on Azure, with Docker containers for

Qualifications

• Master's degree in computer science, robotics, applied mathematics, engineering, or a related field.
• 3-4 years of professional experience.
• Strong software engineering skills, particularly Python; C++ experience is a plus.
• Experience with optimization-based estimation and graph optimization.
• Experience processing large-scale vehicle traces or trajectories.
• SLAM and mapping knowledge with real sensor data.
• Experience with cloud platforms like Azure and Databricks.
• Proficient in deploying Docker containers.

Responsibilities

• Design and implement scalable algorithms that align, filter, and aggregate large-scale crowd-sourced vehicle traces into consistent geometric evidence.
• Build and optimize the lane graph itself: formulate lane-centerline extraction, connectivity, and topology inference as optimization problems and solve them at production scale.
• Combine deterministic and learned methods: use LP/ILP, dynamic programming, and probabilistic estimation with ML where it outperforms.
• Architect high-performance implementations of these algorithms for production-ready deployment across continental scopes.
• Collaborate with cross-functional teams to integrate trace-processing and lane-graph models into production pipelines.
• Stay current with state-of-the-art in trace-based mapping, crowd-sourced map inference, SLAM, and geospatial deep learning.
• Deploy solutions using Docker containers on cloud platforms such as Azure.

Skills

Python

C++

Graph optimization

Optimization-based estimation

SLAM

ML for graphs

Azure

Databricks

Docker

Trace processing

Team collaboration

Education

Master's degree in CS/ robotics/ applied math/ engineering

Tools

Azure

Databricks

Docker

Job description

TomTom is seeking a Software Engineer to design and implement scalable graph-based lane-graph extraction and optimization algorithms. You will transform crowd-sourced traces into a coherent lane graph and integrate both deterministic and learned methods.

The role emphasizes production-grade performance and cross-functional collaboration across ADAS & ADS teams. You will work with Python and C++, apply SLAM-aware mapping techniques, and deploy solutions on Azure, with Docker containers for