Principal Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles
at Nvidia
USD 272,000-431,200 per year
Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Python @ 7
Leadership @ 4
Communication @ 6
Technical Leadership @ 4
Experimentation @ 4
PyTorch @ 7
CUDA @ 4
GPU @ 4
Deep Learning @ 4
AI @ 4
Computer Vision @ 7
Robotics @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Intelligent machines powered by artificial intelligence are transforming every industry. NVIDIA seeks a Principal Perception Engineer to lead the design and productization of next-generation autonomous driving perception stacks, focusing on 3D obstacle perception, modern transformer-based and multi-modal techniques, and production-grade deployment for real-world autonomous vehicles.
Responsibilities
- Own the technical vision, architecture, and roadmap for 3D obstacle perception to support end-to-end autonomous driving functionalities.
- Design and develop advanced 3D perception models using multi-camera inputs and/or multi-sensor fusion (camera, radar, lidar) for obstacle detection and tracking; explore BEV and transformer-based 3D perception.
- Lead development of efficient, production-grade deep learning models: define objectives, select architectures, guide experimentation, and establish best practices for training and evaluation (including large-scale pretraining, distillation, and parameter-efficient fine-tuning such as LoRA).
- Define and drive KPI frameworks to quantify perception performance; analyze large-scale real and synthetic datasets to identify failure modes and improve accuracy, robustness, and efficiency; incorporate self-supervised and representation learning where beneficial.
- Lead data strategy for perception: specify data and labeling requirements, prioritize collection and annotation, and collaborate with data and ground-truth teams; adopt model-assisted workflows (active learning, auto-labeling, VLMs) and model-in-the-loop tooling.
- Partner with safety, systems, and software teams to meet product requirements for safety, latency, resource usage, and software robustness; prepare perception solutions for deployment at scale.
- Provide technical leadership and mentorship to engineers across perception and autonomy teams.
Requirements
- 15+ years of hands-on experience developing deep learning–based perception or closely related systems for complex real-world problems.
- Demonstrated technical leadership as a senior or principal-level individual contributor: owning features/subsystems end-to-end, setting technical direction, making architectural decisions, and coordinating across teams.
- Proven experience in data-driven development and close collaboration with data, labeling, and ground-truth teams.
- Strong proficiency with deep learning frameworks such as PyTorch and a track record of taking models from prototype to production.
- Strong programming skills in Python and/or C++, with experience building reliable, high-performance, production-quality software.
- Excellent communication and collaboration skills; ability to influence and drive consensus across multidisciplinary teams.
- BS/MS/PhD in Computer Science, Electrical Engineering, or related fields (or equivalent experience).
Ways to Stand Out
- Track record designing and deploying 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; optimizing for latency, memory, and compute constraints.
- Familiarity with modern architectures (CNNs, transformers), large-scale pretraining, parameter-efficient fine-tuning (e.g., LoRA), and vision-language models (VLMs).
- Strong publication record or recognized contributions in deep learning, computer vision, or autonomous systems (CVPR, ICCV, NeurIPS, IROS, etc.).
- Deep understanding of 3D computer vision fundamentals: camera modeling and calibration (intrinsic/extrinsic), multi-view geometry, and 3D representations applied to transformer-based 3D or BEV pipelines.
- Experience with CUDA development and optimizing training/inference via custom CUDA kernels or other GPU-accelerated components.
Compensation & Benefits
- Base salary range: 272,000 USD - 431,250 USD (determined by location, experience, and peer pay).
- Eligible for equity and employee benefits (link to NVIDIA benefits referenced in posting).
Other details
- Applications accepted at least until March 16, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and committed to fostering a diverse work environment.