Senior Machine Learning Engineer - Mapping

General Motors; Austin (TX); Hybrid; USD 171,000 - 261,000; Full time

Benefits offered by this job

Health insurance

GM vehicle discounts

Paid time off

Job summary

General Motors is seeking a Senior Software Engineer to design and build mapping algorithms and geospatial data systems for automated map reconstruction and maintenance within the Mapping Engineering team.

You will own the geometric and topological correctness of map features, merge disparate sources, and detect errors before they reach vehicles, applying ML where it measurably improves accuracy and automation. This is a hands-on IC role with cross-team collaboration.

Qualifications

• 3+ years of software engineering experience building production systems with a focus on mapping, geospatial, or geometric algorithms.
• Strong foundation in geospatial and computational geometry concepts—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 (lanes, segments, intersections, topology).
• Hands-on experience with machine learning or computer vision workflows in production—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 CS, GIS, EE, robotics, or related technical field, or equivalent industry experience.
• Ability to own ambiguous, well-scoped technical problems end to end and drive them to production.

Responsibilities

• Design and implement mapping algorithms for map reconstruction and maintenance—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 production map releases on a recurring cadence.
• Apply ML and CV models—detection, segmentation, 3D reconstruction, BEV representations—to automate feature extraction and map change detection, and integrate them into production pipelines.
• Build automated quality, validation, and regression systems that catch geometric, topological, and semantic map defects before release, with clear accuracy metrics.
• Collaborate cross-functionally with Perception, Localization, Simulation, and Platform teams on interfaces, data contracts, and integration points.
• Diagnose and resolve system-level issues spanning geospatial data pipelines, algorithms, models, and production workflows.
• Contribute to design reviews, engineering best practices, and mentorship of engineers on the team.

Skills

Python

C++

Geospatial concepts

Graph algorithms

Machine learning in production

Distributed data pipelines

Education

BS/MS in CS, GIS, EE, robotics, or related field

Tools

PostGIS

GeoPandas

GDAL/OGR

S2/H3

OSM

GeoJSON

Job description

About the Team

Our Mapping organization is building national-scale, next-generation mapping systems that move beyond static HD maps toward automated, algorithmic and ML-assisted map reconstruction pipelines powered by onboard sensor data. These systems form a critical foundation for localization, perception, simulation, and autonomy at scale.

Job Description

About the Team: Our Mapping organization is building national-scale, next-generation mapping systems that move beyond static HD maps toward automated, algorithmic and ML-assisted map reconstruction pipelines powered by onboard sensor data. These systems form a critical foundation for localization, perception, simulation, and autonomy at scale.

The Role: We are looking for a Senior Software Engineer to design and build the mapping algorithms and geospatial data systems behind automated map reconstruction and maintenance within our Mapping Engineering team.

In this role, you will develop the algorithms and data models that reconstruct, conflate, validate, and maintain map primitives - lanes, lane connectivity, road network graphs, boundaries, traffic controls, and signs - from large-scale multi-modal sensor data. You will own the geometric and topological correctness of the map itself: how features are represented, how disparate sources are merged, and how errors are detected before they reach the vehicle.

This is a hands-on individual contributor role with strong ownership. You will drive technical problems end to end, partner closely with Perception, Localization, and Simulation, and apply machine learning where it measurably improves accuracy, coverage, or automation rate.

What you’ll do (Responsibilities)

• Design and implement mapping algorithms for map reconstruction and maintenance - 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 production map releases on a recurring cadence.
• Apply machine learning and computer vision models - detection, segmentation, 3D reconstruction, BEV representations - to automate feature extraction and map change detection, and integrate them into production pipelines.
• Build automated quality, validation, and regression systems that catch geometric, topological, and semantic map defects before release, with clear accuracy metrics.
• Collaborate cross-functionally with Perception, Localization, Simulation, and Platform teams on interfaces, data contracts, and integration points.
• Diagnose and resolve system-level issues spanning geospatial data pipelines, algorithms, models, and production workflows.
• Contribute to design reviews, engineering best practices, and mentorship of engineers on the team.

Minimum Qualifications (Must-Have)

• 3+ years of software engineering experience building production systems, with a substantial portion focused on mapping, geospatial, or geometric algorithms.
• Strong applied foundation in geospatial and computational geometry concepts - coordinate systems and projections, spatial indexing, geometry operations, map matching, and graph algorithms on road networks.
• Demonstrated experience designing geospatial data models and working with road network or map data structures (lanes, segments, intersections, topology).
• Hands-on experience with machine learning or computer vision workflow in production - 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.

Preferred Qualifications (nice To Have)

• Experience with HD maps, localization, perception, or robotics systems, particularly in autonomous driving or mobile robotics.
• Hands-on experience with 3D geometry, multi-view geometry, point cloud processing, or SLAM.
• Familiarity with AV sensor data (camera, lidar, 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 (PostGIS, GeoPandas, GDAL/OGR, S2/H3, OSM, 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.
• 3+ years of software engineering experience building production systems, with a substantial portion focused on mapping, geospatial, or geometric algorithms.

Compensation

• The salary range for this role is $170,600.00 to $261,300.00. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
• Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.

Benefits

• Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}. Relocation benefits are available for candidates who qualify under company policy.

About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us

We believe we all must make a choice every day - individually and collectively - to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.

Benefits Overview

From day one, we're looking out for your well-being - at work and at home - so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.

Non-Discrimination and Equal Employment Opportunities (U.S.)

General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.