Senior Perception Engineer - Autonomous Vehicles

Details

Intelligent machines powered by artificial intelligence computers that can learn, reason, and interact with people are no longer science fiction. GPU deep learning provides the foundation for machines to learn, perceive, reason, and solve problems. NVIDIA's GPU runs deep learning algorithms that simulate human intelligence and acts as the brain of computers, robots, and self-driving cars that can perceive and understand the world.

As a Senior Perception Engineer, you will develop and productize NVIDIA's autonomous driving solutions. You will work on building 3D obstacle perception solutions based on multi-sensor fusion, including cameras, ultrasonic sensors, and radar, to estimate high-resolution reconstructions of the world. The primary approach will be deep learning. The role focuses on improving solution robustness, accuracy, and efficiency to enable autonomous driving anywhere and anytime.

Responsibilities

• Develop multi-sensor-fusion-based deep learning models for obstacle perception and fusion in complex driving environments.
• Conduct applied research and development of deep learning and multi-sensor-fusion algorithms to improve the accuracy of 3D obstacle perception solutions in challenging and diverse scenarios.
• Identify and analyze the strengths and weaknesses of 3D obstacle perception solutions using large-scale real and synthetic benchmark data.
• Improve solutions iteratively through KPI development and optimization, including data verification, model architecture design, loss-function engineering, and detailed machine learning debugging.
• Productize 3D obstacle perception solutions by meeting requirements for safety, latency, and software robustness, with a strong emphasis on production deep learning model development.
• Drive and prioritize data-driven development in collaboration with large data collection and labeling teams.
• Plan data collection and labeling priorities to maximize the value of data for improving perception system accuracy.

Requirements

• 10+ years of hands-on experience developing deep learning and algorithms for sophisticated real-world problems.
• Proficiency with deep learning frameworks such as PyTorch.
• Experience with multi-sensor fusion involving cameras, ultrasonic sensors, and radar for perception tasks, particularly high-resolution world reconstruction.
• Proven experience in production deep learning model development, including data verification, model architecture design, loss-function engineering, and machine learning model debugging.
• Experience with data-driven development and collaboration with data and ground-truth teams.
• Strong programming skills in Python and/or C++.
• Outstanding communication and teamwork skills.
• BS, MS, or PhD in computer science, electrical engineering, sciences, or a related field, or equivalent experience.

Preferred Qualifications

• Experience with end-to-end deep learning model development.
• Expertise developing deep learning perception solutions for autonomous driving or robotics using multi-sensor input.
• Hands-on experience developing and deploying DNN-based solutions to embedded platforms for real-time applications.
• Good understanding of 3D computer vision fundamentals, camera calibration including intrinsic and extrinsic parameters, and sensor-fusion principles.
• Experience developing in CUDA and implementing CUDA kernels as part of training or inference pipelines.

Compensation And Additional Information

• Base salary range for Level 4: USD 184,000–287,500 per year.
• Base salary range for Level 5: USD 224,000–356,500 per year.
• Base salary is determined based on location, experience, and the pay of employees in similar positions.
• Eligible for equity and benefits.
• Applications will be accepted at least until August 1, 2026.
• NVIDIA uses AI tools in its recruiting processes.
• NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.