What you'll be doing:

Integrate and adapt NVIDIA DRIVE parking software on customer development and production vehicles.
Work on vehicle bring-up, software flashing, data logging, testing and functional validation.
Triage and analyze issues across the parking chain, from sensor input through perception and fusion to maneuver behavior.
Collaborate with customer teams and NVIDIA engineering teams worldwide to identify root causes and deliver program-level solutions.
Configure and tune parking software for different vehicle platforms, sensor configurations, vehicle geometries, and low-speed maneuvering requirements.
Debug fusion and environment-modeling behavior combining sensor and neural-network outputs.
Implement targeted C++ or Python adaptations, debugging tools, and analysis workflows to resolve customer integration and functional issues.
Measure functional performance through recorded-data replay and in-vehicle testing, communicating findings clearly.

Requirements:

Strong background in ADAS software development and triage with at least 8+ years of experience including in-vehicle activities.
Strong programming skills in modern C++ and Python.
Proficiency in Linux or QNX-based embedded environments.
Ability to systematically isolate system-level issues through log analysis, recorded-data replay, and in-vehicle testing.
Clear technical communication skills with customers and distributed teams.
BS/MS degree in Computer Science, Electrical Engineering, Robotics, Automotive Engineering, or equivalent experience.
Comfort working in customer-facing field environments with shifting priorities.
Valid driver's license and readiness for vehicle testing and international travel.

Experience with parking or low-speed maneuvering software and related perception, fusion, or environment modeling work.
Production experience with camera- and ultrasonic-based parking or low-speed assistance systems.
Experience integrating ADAS software into customer vehicle platforms and supporting automotive series programs.
Practical experience turning sensor and perception outputs into usable results through fusion, tracking, filtering, or temporal association.
Experience with data-driven development loops including fleet-data triage, corner-case analysis, and model-training feedback.
Hands-on experience with AI/ML workflows and deep neural network development or integration in perception tasks such as object detection, segmentation, or depth estimation.