Principal Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles

at Nvidia
USD 272,000-431,200 per year
SENIOR
✅ On-site

Used Tools & Technologies

Not specified

Required Skills & Competences

Python @ 7 Algorithms @ 4 Hiring @ 4 Leadership @ 4 Communication @ 4 Technical Leadership @ 4 Experimentation @ 4 PyTorch @ 7 CUDA @ 4 GPU @ 4 Deep Learning @ 4 AI @ 4 Computer Vision @ 7 Robotics @ 4

Details

Intelligent machines powered by artificial intelligence are transforming every industry. NVIDIA seeks an exceptional Principal Perception Engineer to lead the design and productization of next-generation autonomous driving perception stack. This is a senior individual contributor role with broad technical leadership, deep hands-on work in architecture, algorithms, and implementation, and a focus on modern transformer-based, multi-modal, and vision-language techniques where they add real value.

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 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 improve accuracy, robustness, and efficiency; incorporate self-supervised and representation learning when beneficial.
  • Lead data strategy for perception: specify data and labeling requirements, prioritize data collection and annotation, and collaborate with data and ground-truth teams; enable model-assisted workflows (active learning, auto-labeling, VLMs) and model-in-the-loop tooling.
  • Partner with safety, systems, and software teams to ensure perception solutions meet product requirements for safety, latency, resource usage, and robustness and are ready for deployment at scale.
  • Provide technical leadership and mentorship to other engineers and influence design and implementation 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.
  • Strong proficiency in frameworks such as PyTorch and a track record of taking models from prototype to production.
  • Demonstrated technical leadership as a senior/principal individual contributor: owning 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 programming skills in Python and/or C++, with history of building reliable, high-performance, production-quality software.
  • BS/MS/PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).
  • Excellent communication and collaboration skills.

Ways to stand out from the crowd (preferred)

  • 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; optimization for latency, memory, and compute constraints.
  • Experience with modern architectures (CNNs, transformers) and techniques like large-scale pretraining, parameter-efficient fine-tuning (LoRA), or vision-language models (VLMs).
  • Strong publication record or recognized contributions in deep learning, computer vision, or autonomous systems (CVPR, ICCV, NeurIPS, IROS).
  • Deep understanding of 3D computer vision fundamentals: camera modeling and calibration (intrinsic/extrinsic), multi-view geometry, and 3D representations, ideally applied in transformer-based 3D or BEV perception pipelines.
  • Experience with CUDA development and optimizing training/inference through custom CUDA kernels or other GPU-accelerated components.

Benefits

  • Base salary range: 272,000 USD - 431,250 USD.
  • Eligible for equity and company benefits (link provided in original posting).

Additional information

  • Applications for this job will be accepted at least until July 9, 2026.
  • NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer committed to inclusive hiring.