Intelligent machines powered by artificial intelligence—computers that can learn, reason, and interact with people—are transforming every industry. GPU-accelerated deep learning provides the foundation for machines to perceive, reason, and solve complex problems. NVIDIA GPUs run deep learning algorithms that simulate aspects of human intelligence. They act as the brain of computers, robots, and self-driving cars. These machines can perceive and interpret their surroundings.

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 build, architecture, and roadmap for 3D obstacle perception to support end-to-end autonomous driving. Use innovative CNN and transformer-based architectures when 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 by defining objectives with the team. Select and prototype architectures, run experiments, and follow training and evaluation guidelines. Use techniques like 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 by specifying data and labeling requirements. Help prioritize data collection and annotation. Collaborate with data and ground-truth teams, including model-assisted workflows such as active learning, auto-labeling, and multimodal AI systems combining vision and language. Also work with 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.

What we need to see:

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 validation 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. This includes optimizing for latency, memory, and compute constraints. Experience with modern architectures such as CNNs and transformers is required. Familiarity with methods such as extensive pretraining, efficient tuning of parameters (e.g., LoRA), or vision-language models (VLMs) is also needed.
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.

What you'll be doing:

Develop and improve the technical build, architecture, and roadmap for 3D obstacle perception to support end-to-end autonomous driving. Use innovative CNN and transformer-based architectures when 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 by defining objectives with the team. Select and prototype architectures, run experiments, and follow training and evaluation guidelines. Use techniques like 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 by specifying data and labeling requirements. Help prioritize data collection and annotation. Collaborate with data and ground-truth teams, including model-assisted workflows such as active learning, auto-labeling, and multimodal AI systems combining vision and language. Also work with 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.

What we need to see:

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 validation 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. This includes optimizing for latency, memory, and compute constraints. Experience with modern architectures such as CNNs and transformers is required. Familiarity with methods such as extensive pretraining, efficient tuning of parameters (e.g., LoRA), or vision-language models (VLMs) is also needed.

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.