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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Perception Engineer, Obstacle Foundation Models - **Company:** NVIDIA Ltd. - **Location:** United States (Remote available) - **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, Deep Learning, Data Strategy, Information Technology, Data Analytics, Feature Extraction, GPT - **Published:** October 4, 2026 - **Apply:** https://startup.jobs/senior-perception-engineer-obstacle-foundation-models-autonomous-vehicles-2100-nvidia-usa-8774952 ## 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. #AutonomousVehicles ## 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. ## Related Videos - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Intelligent Data Selection for Continual Learning of AI Functions](https://www.wearedevelopers.com/videos/367-intelligent-data-selection-for-continual-learning-of-ai-functions) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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