Senior Software Engineer, Multi-Sensor Fusion at NVIDIA

Shanghai, Shanghai, China -

Full Time

Start Date

Immediate

Expiry Date

14 Oct, 26

Salary

0.0

Posted On

16 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Multi-Sensor Fusion, Obstacle Perception, C++, Data Association, Tracking Algorithms, Autonomous Driving, Robotics, Camera Sensors, Radar Sensors, Lidar Sensors, Ultrasonic Sensors, Multi-View Geometry, Occupancy Grid Tracking, Deep Learning, Software Productization, Performance Metrics

Industry

Computer Hardware Manufacturing

Description

Intelligent machines powered by Artificial Intelligence computers that can learn, reason, and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. Now, NVIDIA’s GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. We are now looking for an extraordinary senior software engineer in multi-sensor fusion for obstacle perception to develop and productize NVIDIA's autonomous driving solutions. As a member of our perception team, you will work on building world-class solutions for obstacle perception by tracking and fusing multiple frames from multiple sources of detections. You will be challenged to improve robustness and accuracy as well as efficiency of the solutions to fully enable autonomous driving anywhere and anytime. What You'll Be Doing: Design and develop a robust tracking and fusion algorithm that improves the accuracy and consistency of the detection of dynamic obstacles, moving or stationary, such as vehicles, VRUs, cones and barrels, and road hazards. Identify and analyze the strength and weakness of the developed obstacle perception and fusion under complicated and diverse scenarios. Productize the developed perception solutions by meeting product requirements for safety, latency, and SW robustness. Define and develop meaningful performance metrics for the perception of dynamic obstacles, which guide the algorithm iterations and prevent regression with large-scale offline data sets. What We Need To See: BS/MS/PhD in CS, EE, sciences or related fields (or equivalent experience) 5+ years of total experience in related fields of SW development, with the most recent 3+ years of hands-on work experience in developing perception and fusion algorithms to solve sophisticated real world problems. Familiar with common data association and fusion algorithms. Experience in data-driven development and collaboration with data and ground truth teams. Strong programming skills in modern C++. Outstanding communication and teamwork skills as we work as a tightly-knit team, always discussing and learning from each other. Ways To Stand Out From The Crowd: Proven expertise in developing perception solutions for autonomous driving or robotics. Hands-on experience in Camera, Radar, Lidar and/or ultrasonic sensors. Background in multi-view geometry and occupancy grid tracking is a plus, but not required. NVIDIA has some of the most forward-thinking and hardworking people in the world. If you're creative and autonomous, we want to hear from you! We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation. NVIDIA pioneered accelerated computing. Today, our AI infrastructure powers global intelligence, transforming every industry. Learn more about NVIDIA.

Responsibilities

Design and develop robust tracking and fusion algorithms to improve the detection of dynamic obstacles for autonomous driving. Productize these solutions by ensuring they meet safety, latency, and robustness requirements while defining performance metrics for iteration.