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# Senior Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $184,000.0 - $287,500.0
- **Contract:** Permanent contract
- **Skills:** Algorithm Design, Architectural Patterns, Computer Vision, C++ (Programming Language), Nvidia CUDA, Computer Programming, Computer Engineering, Python (Programming Language), Language Modeling, Sensor Fusion, Pytorch, Delivery Pipeline, Large Language Models, Deep Learning, Data Strategy, Information Technology, Data Analytics, GPT
- **Published:** August 28, 2026
- **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Perception-Engineer--Obstacle-Foundation-Models---Autonomous-Vehicles_JR2021078

## About the Role

* 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.
 * 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.
 * 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 experience building reliable, high-performance, production-quality software.
 * Excellent communication and collaboration skills, with the ability to work effectively across multidisciplinary teams.

Ways to stand out from the crowd:

 * Experience 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, 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.

## Description

We are seeking an exceptional Senior Perception Engineer to help design and productize NVIDIA's next-generation autonomous driving perception stack. You will work on the core 3D obstacle perception pipeline, contribute to architecture and algorithm design, and remain deeply hands-on with implementation, including modern transformer-based, multi-modal, and vision-language techniques where they add real value.

What you'll be doing:

 * Develop and improve the technical design, 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 implement 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.
 * Build efficient, production-grade deep learning models: define objectives with the team, select and prototype architectures, run experiments, and follow best practices for training and evaluation, using techniques such as large-scale pretraining, distillation, and parameter-efficient fine-tuning (e.g., LoRA).
 * Help define and maintain 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 approaches like self-supervised and representation learning when beneficial.
 * Contribute to the data strategy for perception: specify data and labeling requirements, help prioritize data collection and annotation, and collaborate with data and ground-truth teams, including model-assisted workflows (e.g., active learning, auto-labeling, vision-language models (VLMs)) and model-in-the-loop tooling.
 * Collaborate with safety, systems, and software teams to ensure perception solutions meet product requirements for safety, latency, resource usage, and software robustness, and are ready for deployment at scale.


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