Principal Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles

NVIDIA•Santa Clara, CA

About The Position

Intelligent machines powered by artificial intelligence—computers that can learn, reason, and interact with people—are transforming every industry. GPU-accelerated deep learning provides the foundation for machines to perceive, reason, and solve complex problems. NVIDIA GPUs run deep learning algorithms that simulate aspects of human intelligence, acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. We are seeking an exceptional Principal Perception Engineer to lead the design and productization of NVIDIA’s next-generation autonomous driving perception stack. This is a senior individual contributor role with broad technical leadership. You will set the technical direction for 3D obstacle perception, drive cross-functional execution, and mentor other engineers, while remaining deeply hands-on with architecture, algorithms, and implementation, including modern transformer-based, multi-modal, and vision-language techniques where they add real value.

Requirements

15+ years of hands-on experience developing deep learning–based perception or closely related systems for complex real-world problems, with strong proficiency in frameworks such as PyTorch and a track record of taking models from prototype to production.
Demonstrated technical leadership as a senior or principal-level individual contributor: owning features or subsystems end-to-end, setting technical direction, making architectural decisions, and coordinating across teams.
Proven experience in data-driven development, including close collaboration with data, labeling, and ground-truth teams on data strategy, labeling quality, and iterative model improvement.
Strong programming skills in Python and/or C++, with a history of building reliable, high-performance, production-quality software.
Excellent communication and collaboration skills, with the ability to influence, align, and drive consensus across multidisciplinary teams.
BS/MS/PhD in Computer Science, Electrical Engineering, or related fields (or equivalent experience).

Nice To Haves

Proven track record leading the design and deployment of perception solutions for autonomous driving or robotics using camera-based deep learning at scale.
Hands-on experience architecting and deploying DNN-based perception pipelines on embedded or real-time platforms, including optimization for latency, memory, and compute constraints, and experience with modern architectures such as CNNs and transformers, plus familiarity with techniques like large-scale pretraining, parameter-efficient fine-tuning (e.g., LoRA), or vision-language models (VLMs).
Strong publication record or recognized contributions in deep learning, computer vision, or autonomous systems at leading conferences/journals (e.g., CVPR, ICCV, NeurIPS, IROS).
Deep understanding of 3D computer vision fundamentals, including camera modeling and calibration (intrinsic and extrinsic), multi-view geometry, and 3D representations, ideally with experience applying these concepts in transformer-based 3D or BEV perception pipelines.
Experience with CUDA development and optimizing training or inference pipelines through custom CUDA kernels or other GPU-accelerated components.

Responsibilities

Own the technical vision, architecture, and roadmap for 3D obstacle perception to support end-to-end autonomous driving functionalities, leveraging state-of-the-art CNN and transformer-based architectures where appropriate.
Design and develop advanced 3D perception models using multi-camera inputs and/or multi-sensor fusion (camera, radar, lidar) for obstacle detection and tracking, including opportunities to explore BEV and transformer-based 3D perception.
Lead the development of efficient, production-grade deep learning models: define objectives, select architectures, guide experimentation, and establish best practices for training and evaluation, using techniques such as large-scale pretraining, distillation, and parameter-efficient fine-tuning (e.g., LoRA).
Define and drive KPI frameworks to quantify perception performance; analyze large-scale real and synthetic datasets to identify failure modes and systematically improve accuracy, robustness, and efficiency, incorporating modern approaches like self-supervised and representation learning when beneficial.
Lead data strategy for perception: specify data and labeling requirements, prioritize data collection and annotation, and collaborate closely with data and ground-truth teams to maximize impact, including model-assisted workflows (e.g., active learning, auto-labeling, VLMs) and advanced model-in-the-loop tooling.
Partner with safety, systems, and software teams to ensure perception solutions meet stringent product requirements for safety, latency, resource usage, and software robustness, and are ready for deployment at scale.
Provide technical leadership and mentorship to other engineers, influencing design and implementation across the broader perception and autonomy teams.