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.

What You'll Do

Design and implement scalable algorithms that align, filter, and aggregate large-scale crowd-sourced vehicle traces (GPS, IMU, camera-derived observations) into consistent geometric evidence: map-matching, trace clustering, outlier rejection, and drift correction at fleet scale.

Build and optimize the lane graph itself: formulate lane-centerline extraction, connectivity, and topology inference as optimization problems (linear and non-linear programming, graph-based optimization, factor graphs / pose-graph techniques from SLAM) and solve them at production scale.

Combine deterministic and learned methods deliberately: use LP/ILP, dynamic programming, and probabilistic estimation where guarantees and explainability matter, and modern ML (learned detectors, GNNs, transformer-based sequence models over traces) where they demonstrably outperform: own the evaluation that decides which.

Architect high-performance implementations of these algorithms, making them robust, performant, and scalable for production-ready deployment across continental map scopes.

Collaborate with cross-functional teams to integrate trace-processing and lane-graph models into the production map-making pipelines, and feed measured failure cases back into algorithm improvement.

Stay current with the 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.

What You'll Need

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 software engineering skills, particularly Python; C++ experience for performance-critical solver and geometry code is a plus.

3-4 years of hands-on experience with optimization-based estimation: linear and non-linear programming, graph optimization (factor graphs, pose graphs, ILP on network structures), and probabilistic state estimation (Kalman/particle filters, MAP estimation).

Demonstrated experience processing large-scale vehicle trace or trajectory data: map-matching, trace alignment and registration, clustering, and sensor fusion from crowd-sourced or fleet sources (e.g., FCD/probe data, dashcam-derived observations).

SLAM and mapping knowledge: hands-on experience with SLAM or SLAM-adjacent pipelines (visual/lidar odometry, loop closure, bundle adjustment, HD map construction) applied to real sensor data.

Working knowledge of modern ML applied to geometric or graph problems (graph neural networks, learned lane/topology detection, sequence models over trajectories) and sound judgment about when a learned component belongs in a deterministic pipeline.

Experience with cloud computing platforms such as Azure and Databricks.

Proficient in deploying applications using Docker containers.

Ability to think end-to-end and deliver high-quality solutions.

What we offer
A competitive compensation package, of course.  
Time and resources to grow and develop, including a personal development budget and paid leave for learning days, as well as paid access to e-learning resources such as O’Reilly and LinkedIn Learning. 
Time to support life outside of work, with enhanced parental leave plus paid leave to care for loved ones and volunteer in local communities.  
Work flexibility, where TomTom’ers, in agreement with their manager and team, use both the office and home to focus, collaborate, learn and socialize. It’s all about getting the best out of both worlds – we ask TomTom’ers to come to the office two days a week, and the remaining three are free to be worked in either location.
Improve your home office with a setup budget and get extra support with a monthly allowance. 
Enjoy options to work from your home country and abroad for a set number of days each year, to visit family and friends, or to simply explore the world we’re mapping.  
Take the holidays you want with a competitive holiday plan, plus an extra day off to celebrate your birthday. 
Join annual events like our Hackathon and DevDays to bring your ideas to life with talented teammates from around the world. 
Become a part of our inclusive global culture and have the chance to collaborate with a diverse community – we have over 80 nationalities at TomTom! 
Find out more about our global benefits and enjoy additional local benefits tailored to your location. 

Meet your team
We are the ADAS & ADS Product Unit, leading the production of TomTom’s HD maps and ADAS technology.  In a diverse team of applied scientists, engineers, data scientists, and more, equipped with a broad array of expertise, you’ll collaborate on groundbreaking location-based technologies and applications.  More specifically, you’ll be at the forefront of the creation of advanced HD maps. You’ll also help update these in real-time, ensuring our maps are pushing the world forward instantly. These maps will then go on to empower the largest car manufacturers, transportation giants, and major tech companies around the world.

At TomTom...
You’ll help people find their way in the world. In 2004, TomTom revolutionized how the world moves with the introduction of the first portable navigation device. Now, we intend to do it again by engineering the first-ever real-time map, the smartest and most useful map on the planet.
Work with a team of 3,300+ unique, curious and passionate problem-solvers. Together, we’ll open up a world of possibilities for car manufacturers, enterprises and developers to help people understand and get closer to the world around them.

After you apply
Our recruitment team will work hard to give you a meaningful experience throughout your journey with us, no matter the outcome. Your application will be screened closely and you can rest assured that all follow-up actions will be thorough, from assessments and interviews all the way through onboarding. To find out more about our application process, check out our hiring FAQs.

TomTom is an equal opportunity employer
TomTom is where you can find your place in the world. Every day we welcome, nurture and celebrate differences. Why? Because your uniqueness is what makes you, you. No matter your culture or background, you’ll find your impact at TomTom. Research also shows that sometimes women and underrepresented communities can be hesitant to apply for positions unless they believe they meet 100% of the criteria. If you can relate to this, please know that we’d love to hear from you.