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How much do temporary nvidia autonomous driving jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for temporary nvidia autonomous driving in the United States is $21.37, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $21.63 per hour, depending on experience, location, and employer.

What are Temporary Nvidia Autonomous Driving jobs?

Temporary Nvidia Autonomous Driving jobs are short-term positions at Nvidia focused on developing, testing, or supporting autonomous vehicle technologies. These roles may involve software engineering, data analysis, system testing, or support functions related to self-driving car platforms. Temporary roles are typically project-based, offering opportunities to work on cutting-edge artificial intelligence and robotics applications within the autonomous driving sector. Such positions provide valuable experience in the fast-evolving field of automated vehicles, even if they are not permanent.

What are the key skills and qualifications needed to thrive as a Temporary Nvidia Autonomous Driving Engineer, and why are they important?

To excel as a Temporary Nvidia Autonomous Driving Engineer, you need strong programming skills (especially in C++ and Python), a solid background in robotics or computer vision, and typically a degree in computer science, engineering, or a related field. Experience with autonomous vehicle platforms, Nvidia DRIVE, deep learning frameworks (like TensorFlow or PyTorch), and familiarity with sensor fusion technologies are highly valued. Excellent problem-solving abilities, teamwork, and adaptability are crucial soft skills for integrating new technologies and collaborating across multidisciplinary teams. These competencies ensure safe, innovative solutions in the rapidly evolving field of autonomous vehicles.

What are some common challenges faced by a Temporary Nvidia Autonomous Driving specialist, and how can applicants prepare for them?

Temporary specialists in Nvidia's Autonomous Driving division often encounter fast-paced project timelines and rapidly evolving technical requirements. Adapting quickly to new tools, frameworks, and proprietary systems is essential, as is a willingness to collaborate across multidisciplinary teams such as software engineering, data annotation, and hardware integration. Applicants can prepare by demonstrating strong foundational knowledge in machine learning, computer vision, and robotics, as well as effective communication skills to navigate a dynamic, innovative environment.
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Temporary Nvidia Autonomous Driving jobs near you

Senior Software Engineer - Autonomous Driving

NVIDIA AI

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Posted 18 days ago

Job description

Job Summary:
NVIDIA AI is building the software foundation for scalable, high-performance vehicle computing platforms that power autonomous driving and centralized vehicle architectures. They are seeking a Senior Software Engineer to lead architecture and optimization efforts across the autonomous driving software stack, focusing on deep neural network optimization and deployment on NVIDIA automotive compute platforms.
Responsibilities:
• Lead architecture and technical strategy for optimizing inference workloads in autonomous driving applications.
• Drive end-to-end performance analysis across DNN models, TensorRT/compiler flows, CUDA kernels, memory behavior, scheduling, runtime services, and automotive platform constraints.
• Develop and guide model optimization techniques such as quantization, pruning, distillation, graph optimization, operator fusion, kernel selection, and layout/memory optimization.
• Collaborate with TensorRT, CUDA, compiler, silicon architecture, perception, planning, DriveOS and safety platform teams.
• Build tools, methodologies, and metrics for profiling, benchmarking, debugging, and validating model and platform performance.
Qualifications:
Required:
• BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
• 12+ years of software engineering experience in systems software, AI/ML infrastructure, deep learning inference, compiler/runtime technology, or platform performance.
• Strong C/C++ and practical Python experience.
• Deep familiarity with TensorRT, TensorRT-LLM, ONNX, PyTorch, CUDA, Triton, or related frameworks.
• Experience optimizing DNN models for latency, throughput, memory footprint, and power.
Preferred:
• Hands-on experience with TensorRT internals, CUDA kernels, Triton kernels, or other compiler/runtime technologies.
• Experience deploying optimized DNNs, LLMs, VLMs, or perception models on embedded, edge, robotics, or automotive platforms.
• Background in autonomous driving, ADAS, robotics, real-time systems, safety-aware software, or deterministic low-latency systems.
• Experience with ISO 26262, QNX, Safe RTOS, DriveOS, Linux, hypervisors, or virtualization.
Company:
Explore the latest breakthroughs made possible with AI. Founded in , the company is headquartered in Santa Clara, CA, US, , with a team of 10001+ employees. The company is currently Late Stage.

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