- 15+ years of 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.
- Demonstrated technical leadership as a senior or principal-level individual contributor: owning features or subsystems end-to-end, setting technical direction, making architectural decisions, and coordinating across teams.
- 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 a history of building reliable, high-performance, production-quality software.
- Excellent communication and collaboration skills, with the ability to influence, align, and drive consensus across multidisciplinary teams.
- BS/MS/PhD in Computer Science, Electrical Engineering, or related fields (or equivalent experience).
Ways to stand out from the crowd
- Proven track record leading the design and deployment of 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
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 272,000 USD – 431,250 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.