Senior Machine Learning Engineer, Perception - Autonomous Driving

NVIDIA•Santa Clara, WA

About The Position

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 Perception Engineer to develop and productize NVIDIA’s autonomous driving solutions. As a member of our perception team, you will be driving E2E solutions for perception modules that are responsible for online mapping — including road layouts, lane structures, boundaries, crosswalks, and other traffic components critical for driving without reliance on HD maps. You will be challenged to improve robustness and accuracy as well as efficiency of the solutions to fully enable autonomous driving anywhere and anytime. Intelligent machines powered by AI are no longer science fiction. GPU Deep Learning has made it possible for self-driving cars to learn, perceive, and reason about the world. NVIDIA GPUs power the algorithms that enable both static world understanding and scalable perception across global road systems. Join us and help define the future of reliable, data-driven autonomous driving. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until March 23, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) 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. NVIDIA is the world leader in accelerated computing. NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society. Learn more about NVIDIA.

Requirements

Minimum Requirement: PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
2+ years of technical leadership demonstrating high technical and organizational complexity is a big plus.
Hands-on work experience in developing deep learning and algorithms to solve sophisticated real world problems, and proficiency in using deep learning frameworks (e.g., PyTorch).
Experience in data-driven development and collaboration with data and ground truth teams.
Strong programming skills in python and/or C++.
Outstanding communication and teamwork skills as we work as a tightly-knit team, always discussing and learning from each other.

Nice To Haves

Proven expertise in developing generalizable perception solutions for autonomous driving or robotics using deep learning with cameras.
Hands-on experience in developing and deploying DNN-based solutions to embedded platforms for real time applications.
Proven expertise in deep learning backed up by technical publications in leading conferences/journals.
Expertise with Transformers, BEV architectures, and modern static-world perception techniques.
Experience in working on similar online mapping and complex road detection problems is a big plus.

Responsibilities

Designing end2end solutions for Perception and AV stack to enable road network detections across various driving environments from complex intersections to rural curvy roads to multi-level highways.
Applied research and development of innovative deep learning models for lane graph construction, road boundary detection, traffic element recognition, and other static-world tasks.
Develop generalizable approaches to support diverse ODDs and Country/region expansion
Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy. Efforts will include data collection prioritization and planning, labeling prioritization, labeling efficiency optimization, so that value of data is maximized
Leverage data simulation and augmentation for solving extreme scenarios
Productize the developed perception solutions by meeting product requirements for safety, latency, and SW robustness.