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Senior Machine Learning Engineer – Mapping
General Motors. Design and implement mapping algorithms for map reconstruction and maintenance, including lane and boundary extraction, road network graph construction, map conflation and matching, geometry simplification, and topology validation .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing geospatial algorithms and data models, with a strong foundation in machine learning and computer vision for automated feature extraction and map validation. Proficient in building large-scale distributed data pipelines and mentoring engineering teams in best practices.
Highest-signal resume keywords
Geospatial AlgorithmsMachine Learning WorkflowsDistributed Data PipelinesPython and C++ ProficiencyGeospatial Data Models
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Mapping AlgorithmsGeometric AlgorithmsMachine LearningComputer Vision3D GeometryPoint Cloud ProcessingMap ConflationChange DetectionData ValidationGraph Algorithms
Soft Skills
MentoringProblem SolvingCollaboration
Tools & Technologies
PostGISGeoPandasGDAL/OGRS2/H3OSMGeoJSON
Industry Keywords
Geospatial DataAutonomous DrivingMobile RoboticsSensor DataLocalization
Tech Stack
Tools & technologiesCloudPostGISPythonC++
About the role
Key responsibilities & impact- Design and implement mapping algorithms for map reconstruction and maintenance, including lane and boundary extraction, road network graph construction, map conflation and matching, geometry simplification, and topology validation
- Build and evolve geospatial data models for lane-level connectivity at intersections, road network graphs, and associated map attributes and restrictions
- Develop large-scale distributed geospatial pipelines that process sensor-derived and third-party road data into recurring production map releases
- Apply machine learning and computer vision models, including detection, segmentation, 3D reconstruction, and BEV representations, to automate feature extraction and map change detection
- Integrate machine learning and computer vision models into production pipelines
- Build automated quality, validation, and regression systems to detect geometric, topological, and semantic map defects before release
- Define and track clear accuracy metrics
- Collaborate with Perception, Localization, Simulation, and Platform teams on interfaces, data contracts, and integration points
- Diagnose and resolve system-level issues across geospatial data pipelines, algorithms, models, and production workflows
- Contribute to design reviews and engineering best practices
- Mentor engineers on the team
Requirements
What you’ll need- 3+ years of software engineering experience building production systems, with a substantial portion focused on mapping, geospatial, or geometric algorithms
- Strong foundation in geospatial and computational geometry concepts, including coordinate systems and projections, spatial indexing, geometry operations, map matching, and graph algorithms on road networks
- Experience designing geospatial data models and working with road network or map data structures, including lanes, segments, intersections, and topology
- Production experience with machine learning or computer vision workflows, including dataset curation, model training or fine-tuning, evaluation, and deployment
- Experience building large-scale distributed data pipelines for geospatial or sensor data
- Proficiency in Python and C++
- BS or MS in Computer Science, GIS, Electrical Engineering, Robotics, or a related technical field, or equivalent industry experience
- Ability to own ambiguous, well-scoped technical problems end to end and drive them to production
- Experience with HD maps, localization, perception, or robotics systems, particularly in autonomous driving or mobile robotics
- Experience with 3D geometry, multi-view geometry, point cloud processing, or SLAM
- Familiarity with AV sensor data, including camera, lidar, and radar, and real-world data challenges such as noise, drift, and long-tail scenarios
- Experience with map conflation, change detection, or automated map QA at national or global scale
- Experience with geospatial tooling and formats, including PostGIS, GeoPandas, GDAL/OGR, S2/H3, OSM, and GeoJSON
- Experience deploying ML models into production pipelines with monitoring, validation, and iteration loops
- Experience mentoring engineers or acting as a technical lead on a project
Benefits
Comp & perks- Bonus potential through an incentive pay program based on company performance, job level, and individual performance
- Medical, dental, and vision benefits
- Health Savings Account
- Flexible Spending Accounts
- Retirement savings plan
- Sickness and accident benefits
- Life insurance
- Paid vacation and holidays
- Tuition assistance programs
- Employee assistance program
- GM vehicle discounts
- Relocation benefits for candidates who qualify under company policy