Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Algorithms @ 4
Mathematics @ 6
Debugging @ 4
AI @ 4
Computer Vision @ 4
Robotics @ 6
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA is building planet-scale maps for self-driving technology using crowdsourced data from millions of vehicles. The role involves computer vision, geometry, pose estimation, sensor fusion, and large-scale systems to transform sparse perception signals and dense video into accurate, fresh maps that improve driving performance, safety, and coverage. You will work with engineers in mapping, perception, reconstruction, localization, and autonomous driving to deliver map products used in self-driving and driver support technologies.
Responsibilities
- Build scalable mapping systems using crowdsourced perception data and multi-camera video from millions 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.
- Collaborate closely with perception, localization, simulation, planning, and infrastructure teams to integrate crowdsourced maps into autonomous driving systems.
- Continuously improve the scale, fidelity, freshness, and reliability of maps built from real-world fleet data.
Requirements
- 5+ years of experience and BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Mathematics, or a related technical field (or equivalent experience).
- 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.
Ways to stand out
- 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.
Compensation & Benefits
- Base salary range by level:
- Level 3: 152,000 USD - 241,500 USD
- Level 4: 184,000 USD - 287,500 USD
- Eligible for equity and benefits (see NVIDIA benefits page).
Other information
- Location: Santa Clara, CA, United States.
- Applications accepted at least until June 30, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and values diversity in its workforce.