Principal Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles

NVIDIA Corporation; Santa Clara (CA); Presencial; USD 272 000 - 431 000; Tempo integral

Resumo da oferta

NVIDIA seeks a Principal Perception Engineer to lead the design and productization of the next-generation autonomous driving perception stack. You will own the technical direction for 3D obstacle perception, drive cross-functional execution, and mentor engineers while staying hands-on with architecture, algorithms, and implementation.

Role demands 15+ years in deep learning perception, strong leadership, and mastery of PyTorch, Python, and C++.

Qualificações

• 15+ years hands-on experience in deep learning perception systems.
• Technical leadership as a senior or principal contributor.
• Data-driven development with close collaboration to data and labeling teams.
• Strong Python and C++ production software experience.
• Excellent communication and cross-functional collaboration skills.
• BS/MS/PhD in CS, EE, or related field.

Responsabilidades

• Own the technical vision, architecture, and roadmap for 3D obstacle perception.
• Design and develop advanced 3D perception models with multi-sensor inputs.
• Lead production-grade deep learning models and experimentation.
• Define KPI frameworks and improve accuracy, robustness, and efficiency.
• Lead data strategy, labeling, and model-in-the-loop tooling.
• Collaborate with safety, systems, and software teams for scalable deployment.
• Provide technical leadership and mentorship across perception and autonomy teams.

Conhecimentos

PyTorch

Python

C++

Deep learning

Leadership

Production deployment

3D perception

Autonomous driving

Formação académica

BS/MS/PhD in Computer Science or Electrical Engineering

Ferramentas

CUDA

TensorRT

ROS2

Descrição da oferta de emprego

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.

What you’ll be doing:

• 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.

What we need to see:

• 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).

Ways to stand out from the crowd:

• 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.

#AutonomousVehicles

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 2, 2026.

This posting is for an existing vacancy.

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