Parking Perception DNN Engineer

Details

Intelligent machines powered by Artificial Intelligence 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 GPUs run deep learning algorithms, simulating human intelligence, and act as the brain of computers, robots and self-driving cars that can perceive and understand the world.

We are looking for an extraordinary Senior Perception Engineer to develop and productize NVIDIA's autonomous driving solutions. As a member of the perception team, you will build world-class 3D obstacle perception solutions based on multi-sensor fusion (cameras, ultrasonic sensors, radar) to estimate high-resolution reconstruction of the world (e.g., occupancy networks). The primary approach will be deep learning. You will be challenged to improve robustness, accuracy, and efficiency of solutions 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.
• Perform applied research and R&D of innovative deep learning and sensor-fusion algorithms to improve 3D obstacle perception accuracy under diverse and challenging scenarios, with a focus on high-resolution world reconstruction (e.g., occupancy networks).
• Identify and analyze strengths and weaknesses of developed 3D obstacle perception solutions using large-scale benchmark data (real and synthetic) and iterate improvements through KPI building and optimization.
• Conduct careful data verification, model architecture design, loss function engineering, and ML debugging to improve models.
• Productize developed 3D obstacle perception solutions to meet product requirements for safety, latency, and software robustness, emphasizing production-grade deep learning model development.
• Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high-value data, including data collection prioritization and labeling planning.

Requirements

• 10+ years of hands-on experience developing deep learning and algorithms to solve sophisticated real-world problems.
• Proficiency with deep learning frameworks (e.g., PyTorch).
• Experience in multi-sensor fusion (cameras, ultrasonic sensors, radar) for perception tasks, particularly for high-resolution world reconstruction.
• Proven experience in production deep learning model development, including data verification, model architecture design, loss function engineering, and debugging ML models.
• Experience in data-driven development and collaboration with data and ground-truth teams.
• Strong programming skills in Python and/or C++.
• BS/MS/PhD in Computer Science, Electrical Engineering, sciences or related fields (or equivalent experience).
• Outstanding communication and teamwork skills.

Ways to stand out

• Experience on end-to-end deep learning model development.
• Proven expertise in perception solutions for autonomous driving or robotics using multi-sensor deep learning.
• 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 (intrinsic and extrinsic), and sensor-fusion principles.
• Experience with CUDA and ability to implement CUDA kernels as part of training or inference pipelines.

Compensation & Benefits

• Base salary ranges (determined by location, experience, and comparative pay):
  • Level 4: 184,000 USD - 287,500 USD
  • Level 5: 224,000 USD - 356,500 USD
• Eligible for equity and additional benefits. (See NVIDIA benefits page.)

Other details

• Location: Santa Clara, CA, United States.
• Employment type: Full time.
• Applications for this job will be accepted at least until November 11, 2025.
• NVIDIA is committed to fostering a diverse work environment and is an equal opportunity employer. NVIDIA does not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.