Senior Graph Optimization Scientist - Trace Alignment

TomTom; Amsterdam; Hybrid; EUR 85,000 - 120,000; Full time

Benefits offered by this job

Personal development budget

Learning days paid leave

Access to O’Reilly & LinkedIn Learning

Parental leave

Home office setup budget

Global Hackathons & DevDays

Job summary

TomTom’s Road Features Group develops HD maps and lane graphs for autonomous driving. You will design scalable algorithms to process crowd-sourced traces and build the lane-centerline and topology inference at scale.

Join a cross-disciplinary team applying geometry processing, ML, and SLAM techniques. Expect production-ready deployments on cloud platforms and collaboration across global teams.

Qualifications

• Master's degree or higher in CS, robotics, applied mathematics, or engineering.
• 3–4 years of professional software engineering experience.
• Strong Python; C++ for performance-critical code is a plus.
• Hands-on experience with optimization-based estimation and graph optimization.
• Experience with SLAM, mapping, and processing large-scale traces/data.

Responsibilities

• Design and implement scalable algorithms to align and aggregate large-scale traces into evidence-based maps.
• Build and optimize lane-graph extraction and topology inference at production scale.
• Combine deterministic and learned components, using LP/ILP and probabilistic state estimation.
• Architect high-performance, production-ready implementations for continental map scopes.
• Collaborate with cross-functional teams to integrate trace processing into map pipelines.

Skills

Python

C++

Optimization

Graph optimization

SLAM

ML for geometry

Docker

Azure

Databricks

End-to-end delivery

Education

Master's degree in computer science, robotics, applied mathematics, Engineering

Tools

Docker

Azure

Databricks

Git

Job description

TomTom’s Road Features Group develops HD maps and lane graphs for autonomous driving. You will design scalable algorithms to process crowd-sourced traces and build the lane-centerline and topology inference at scale.

Join a cross-disciplinary team applying geometry processing, ML, and SLAM techniques. Expect production-ready deployments on cloud platforms and collaboration across global teams.