2 days ago

Senior Applied Scientist - Graph Optimization & Trace Alignment

TomTom

Amsterdam

2 days ago

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TomTom's Road Features Group seeks a Senior Applied Scientist to design algorithms for graph optimization, trace alignment, and lane graph construction from crowd-sourced vehicle data. The role combines deterministic optimization with ML, requiring expertise in SLAM, linear programming, and large-scale trace processing. The position is hybrid in Amsterdam.

AIHybridFull-timeSeniorPythonC++

Senior Applied Scientist - Graph Optimization & Trace Alignment

Apply

AIHybridFull-timeSeniorPython

Salary

Not specified

Work Location

Amsterdam, North Holland, Netherlands, NL

Work Model

Hybrid: two days per week in office, three days remote

Experience Required

4 years

Employment Type

Full-time

Experience Level

Senior

Core Qualifications

Technical (Must-have)

PythonC++Linear ProgrammingNon-linear ProgrammingGraph OptimizationFactor GraphsPose GraphsILPDynamic ProgrammingProbabilistic State EstimationKalman FiltersParticle FiltersMAP EstimationSLAMMap-matchingTrace AlignmentSensor FusionGraph Neural NetworksAzureDatabricksDocker

Soft Skills

CollaborationEnd-to-end thinkingSound judgment

Key Responsibilities

•Design and implement scalable algorithms for aligning, filtering, and aggregating crowd-sourced vehicle traces.
•Build and optimize lane graph using optimization problems (LP, NLP, factor graphs).
•Combine deterministic and learned methods for trace processing and lane graph construction.
•Architect high-performance implementations for production deployment.
•Collaborate with cross-functional teams to integrate algorithms into production pipelines.
•Stay current with state of the art in trace-based mapping, SLAM, and geospatial deep learning.
•Deploy solutions using Docker on Azure.

Senior Applied ScientistGraph OptimizationSLAMTrace AlignmentHD MapsAutonomous VehiclesPythonAzureDockerTomTom

Core Qualifications

Technical (Must-have)

Soft Skills

CollaborationEnd-to-end thinkingSound judgment

Key Responsibilities

•Design and implement scalable algorithms for aligning, filtering, and aggregating crowd-sourced vehicle traces.

•Build and optimize lane graph using optimization problems (LP, NLP, factor graphs).

•Combine deterministic and learned methods for trace processing and lane graph construction.

•Architect high-performance implementations for production deployment.

•Collaborate with cross-functional teams to integrate algorithms into production pipelines.

•Stay current with state of the art in trace-based mapping, SLAM, and geospatial deep learning.

•Deploy solutions using Docker on Azure.