# Principal Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles

Hiring organization: [NVIDIA](https://career.thegoodapps.co/organizations/nvidia)

Canonical page: https://career.thegoodapps.co/jobs/bfbc4ba8-349f-4c86-a18a-6c8d4c83b7b7

Listed on NVIDIA's own careers site. Applications go to them directly.

- Seniority: Lead / Staff / Principal
- Salary: 272000 – 431250 USD per year

## Summary

NVIDIA is hiring a principal-level perception engineer to lead the design and deployment of 3D obstacle detection systems for autonomous vehicles, working across deep learning architecture, model optimization, and cross-functional coordination. This role suits someone with 15+ years shipping production perception systems who can balance hands-on technical work with mentoring and setting strategic direction.

_Our summary, not NVIDIA's wording._

## Skills named

C++, Convolutional Neural Networks (CNN), CUDA, Python, PyTorch

## Required

- 15+ years developing deep learning perception systems for real-world problems
- Senior or principal-level individual contributor experience owning features end-to-end
- Data-driven development with collaboration on data strategy and labeling
- Strong Python and/or C++ programming with production software track record
- Communication and collaboration across multidisciplinary teams
- BS/MS/PhD in Computer Science, Electrical Engineering, or equivalent experience

## Nice to have

- Track record leading perception solutions for autonomous driving or robotics at scale
- Experience architecting DNN perception pipelines on embedded or real-time platforms with latency and compute optimization
- Publications or recognized contributions at CVPR, ICCV, NeurIPS, IROS or similar venues
- Deep understanding of 3D geometry and camera modeling applied to transformer or BEV perception
- CUDA development experience optimizing training or inference pipelines

Apply on NVIDIA's site: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Principal-Perception-Engineer--Obstacle-Foundation-Models---Autonomous-Vehicles_JR2020551