Senior Perception AI Engineer - Autonomous Vehicles

Details

Self-driving cars need to understand the road ahead in real time, even without a prior map. The team builds an online mapping system that converts raw data from multiple sensors into an accurate, continuously updated 3D model of the driving environment. The model captures lanes, road boundaries, crosswalks, intersection layouts, and traffic control information to support decisions across the autonomous driving system.

The role involves developing models and production C++ code, then deploying both into vehicles running on NVIDIA DRIVE Orin and Thor. The work is tested at scale on real vehicles under real safety requirements and supports L2++, L3, and L4 autonomous-driving systems.

The role spans two subject areas:

• Learned mapping: Training DNNs to predict map elements directly from visual sensors, laser scanning, and radar, including owning the ground-truth (GT) process and collaborating with data partners.
• Geometry and C++ engineering: Building post-processing that converts network outputs into stable, consistent, production-ready map elements.

Responsibilities

• Design, train, and improve PyTorch models that predict online map elements, including lanes, road boundaries, crosswalks, road markings, and topology, from camera, LiDAR, and/or radar using BEV and vectorized-map representations.
• Model intersection topology, including connectivity between incoming and outgoing lanes, turn paths, merges, and splits.
• Develop traffic-light understanding capabilities, including detection, state recognition, and association of each light with the lanes it controls.
• Write production C++ for geometry-based post-processing, including curve and surface fitting, association, multi-frame fusion and tracking, topology reasoning, and consistency checks on DNN outputs.
• Apply multi-view geometry, calibration, coordinate transforms, and sensor fusion to produce accurate and stable 3D maps.
• Optimize and deploy models and algorithms on NVIDIA DRIVE Orin and Thor under real-time and safety constraints, and deploy them in vehicles.
• Lead the GT and data-loop strategy by planning datasets, defining GT and labeling requirements, and working with labeling, data, and tooling partners to produce high-quality GT.
• Mine data, select metrics, analyze failure cases, and drive model improvements.
• Define critical metrics and build evaluation tooling to measure quality improvements against large-scale real-world data.
• Partner with perception, localization, planning, and infrastructure teams to integrate online mapping into the full autonomous-driving stack across L2++, L3, and L4 programs.
• Contribute to technical direction and architecture decisions across the learned and geometric components of the mapping system.

Requirements

• MS, PhD, or equivalent experience in Computer Science, Electrical Engineering, Robotics, or a related field.
• 5+ years of industry or research experience in autonomous driving, robotics, or computer vision.
• Strong foundation in 3D geometry, including camera models, projective geometry, coordinate transforms, optimization, and curve or surface fitting.
• Strong C++ skills and a track record of designing and shipping fast, reliable, maintainable, production-quality algorithm code.
• Hands-on experience training deep neural networks in PyTorch, including model design, training pipelines, debugging, and performance analysis.
• Experience with data-driven development, including building datasets, defining labels and GT, choosing metrics, and improving models through failure analysis.
• Excellent problem-solving and communication skills, with the ability to drive technical direction across teams.

Preferred Qualifications

• Direct experience with DNN-based online or HD map generation, BEV perception, vectorized map prediction, lane topology learning, or equivalent BEV perception experience.
• Experience with intersection understanding, including lane connectivity and topology reasoning in sophisticated junctions, or traffic-light detection and lane association.
• Experience combining learned models with geometric post-processing in a production autonomous-driving system.
• Publications or open-source contributions in 3D vision, mapping, or autonomous driving.
• Background in sensor fusion, multi-object tracking, or temporal fusion for map elements.
• Experience deploying DNNs and algorithms on embedded GPUs using CUDA and TensorRT, ideally on DRIVE Orin or Thor, under real-time constraints.

Compensation and Benefits

The expected base salary ranges are $184,000–$287,500 for Level 4 and $224,000–$356,500 for Level 5. The position also includes eligibility for equity and benefits.

Applications will be accepted at least until November 13, 2026. NVIDIA is an equal opportunity employer committed to an inclusive work environment.