Senior Mapping ML Engineer (Hybrid/Remote)

General Motors Corporation; Sunnyvale (CA); Hybrid; USD 171,000 - 261,000; Full time

Benefits offered by this job

Hybrid work model

Relocation benefits

Health benefits

Job summary

General Motors is seeking a Senior Machine Learning Engineer to design and implement advanced mapping algorithms and geospatial data systems for scalable map reconstruction and maintenance. You will work on lanes, boundaries, road networks, and topology with ML-enhanced pipelines.

You will own end-to-end development, collaborating with perception, localization, and simulation teams, and deploying models in production with robust validation and quality checks.

Qualifications

• 3+ years of software engineering experience building production systems with a focus on mapping or geospatial algorithms.
• Strong foundation in geospatial and computational geometry concepts (coordinates, projections, spatial indexing, map matching, road networks).
• Hands-on ML/CV workflow in production — data curation, model training or fine-tuning, evaluation and deployment.
• Experience building large-scale distributed geospatial data pipelines for sensor data or maps.

Responsibilities

• Design and implement mapping algorithms for map reconstruction and maintenance (lanes, boundaries, road graphs).
• Develop geospatial data models for lane connectivity and road networks at intersections.
• Build and deploy large-scale geospatial pipelines processing sensor data into production map releases.
• Apply ML/CV models to automate feature extraction and map change detection in pipelines.
• Create automated quality, validation, and regression systems for geometry and topology checks.

Skills

Python

C++

Geospatial algorithms

Distributed systems

Education

BS in CS/GIS/EE/Robotics or related

MS in CS/GIS/EE/Robotics or related

Tools

PostGIS

GeoPandas

GDAL/OGR

S2/H3

OSM

GeoJSON

Job description

General Motors is seeking a Senior Machine Learning Engineer to design and implement advanced mapping algorithms and geospatial data systems for scalable map reconstruction and maintenance. You will work on lanes, boundaries, road networks, and topology with ML-enhanced pipelines.

You will own end-to-end development, collaborating with perception, localization, and simulation teams, and deploying models in production with robust validation and quality checks.