Principal Software Engineer, Mapping - Autonomous Vehicles

NVIDIA•Santa Clara, CA

1d•$272,000 - $431,250

About The Position

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We seek a passionate team member to build planet-scale maps supporting self-driving technology using crowdsourced data from millions of cars around the world. The role involves computer vision, geometry, pose estimation, sensor fusion, and large-scale systems. You will transform sparse perception signals and dense video clips into accurate and fresh maps. Our work helps autonomous vehicles understand the road with high-quality map priors. These maps improve driving performance, safety, and coverage. You will work with a diverse team of engineers in mapping, perception, reconstruction, localization, and autonomous driving. Together, we will deliver impact to customers worldwide.

Requirements

Strong programming skills in C++ and experience building production-quality software systems.
Solid foundation in 3D computer vision, 3D geometry, multi-view geometry, structure from motion, SLAM, pose estimation, or related areas.
Experience working with large-scale sensor data, including camera video, perception outputs, vehicle poses, GPS/IMU signals, lidar, radar, or map data.
Ability to reason about coordinate frames, calibration, uncertainty, optimization, geometric consistency, and error propagation.
Experience crafting algorithms that are robust to noisy real-world data, dynamic objects, occlusions, incomplete coverage, and long-tail failures.
Strong debugging and analytical skills, including the ability to inspect data visually, build metrics, and connect system-level failures to algorithmic root causes.
15+ years of experience
BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Mathematics, or a related technical field (or equivalent experience).

Nice To Haves

Experience building maps, localization systems, 3D reconstruction systems, perception systems, or sensor-fusion pipelines for autonomous driving or advanced driver assistance systems.
Background in large-scale mapping, crowdsourced map construction, map fusion, change detection, map freshness, road topology, lane geometry, or semantic map generation.

Responsibilities

Build scalable mapping systems using crowdsourced perception data and multi-camera video collected from a vast number of vehicles.
Develop 3D reconstruction, structure-from-motion, pose estimation, and multi-view geometry algorithms for large-scale road scene understanding.
Build map fusion and change-detection methods that can handle noisy observations, dynamic scenes, imperfect localization, and global consistency constraints.
Build C++ production systems and offline pipelines that transform fleet data into reliable map products used in self-driving and driver support technologies.
Invent evaluation methods to measure map accuracy, freshness, coverage, consistency, and downstream autonomy impact.
Develop visualization, debugging, and triage tools to understand reconstruction quality, map issues, localization errors, and fleet data gaps.
Work closely with perception, localization, simulation, planning, and infrastructure teams to integrate crowdsourced maps into autonomous driving systems.
Relentlessly improve the scale, fidelity, freshness, and reliability of maps built from real-world fleet data.