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

As of Jun 23, 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 Machine Learning Engineer, EndtoEnd Autonomous Driving

Senior Machine Learning Engineer, EndtoEnd Autonomous Driving

Nvidia

Santa Clara, CA

$122K - $168K/yr

Full-time

Posted 2 days ago

Job description

We are seeking a Senior Machine Learning Engineer to join our endtoend autonomous driving team! You will help build, train, and deploy largescale E2E driving models that leverage VLM/VLA architectures, and build a data flywheel that continuously improves our systems in the real world! Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

What you'll be doing:

  • Designing, implementing, and training largescale endtoend driving models.

  • Driving the data flywheel: identifying failure cases, specifying data collection and labeling needs, and iterating models to close realworld performance gaps.

  • Building, curating, and maintaining highquality multimodal datasets (e.g., video, sensor, language/action traces) tailored for endtoend autonomous driving.

  • Developing and applying datacentric learning algorithms such as active learning, curriculum learning, automated hardexample mining, outlier and novelty detection, and semi/selfsupervised methods.

  • Exploring and productizing new data sources including simulation, synthetic data, and worldmodelbased generation/augmentation to improve coverage and robustness.

  • Designing and implementing agentic data workflows that automate data discovery, labeling, evaluation, and retraining to maximize development velocity.

  • Foster collaborative partnerships with our researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models.

What we need to see:

  • PhD with 4+ years, MS with 6+years, or BS (or equivalent experience) with 8+ years of relevantexperience in Computer Science,Computer Engineering, or a relatedtechnical field

  • Strong background in modern deep learning, including transformerbased architectures, video modeling, and multimodal VLM/VLA or foundation models.

  • Handson experience training and deploying deep learning models on realworld datasets: data preprocessing, distributed training, evaluation, debugging, and iterative improvement.

  • Practical experience with at least some datacentric methods such as active learning, curriculum learning, outlier/novelty detection, or largescale sample mining.

  • Proficiency in Python and at least one major deep learning framework (PyTorch, TensorFlow, or JAX), plus solid software engineering practices (testing, code review, CI/CD).

  • Demonstrated ability to collaborate effectively across teams, drive designs from prototype to production, and communicate clearly with technical and nontechnical partners.

  • Track record of leading complex crossteam projects, setting technical direction, and making critical technical decisions that impact multiple teams or products.

Ways to stand out from the crowd:

  • Experience building and operating data flywheels or largescale data pipelines for ML, including data quality monitoring and continuous retraining loops.

  • Direct experience with endtoend driving models, largescale behavior cloning, or reinforcement/imitation learning for driving or robotics.

  • Experience leveraging simulation, synthetic data, or world models to generate training and evaluation data for autonomous systems.

  • Contributions to sophisticated methods in datacentric ML, VLM/VLA, or autonomous driving, such as impactful publications, opensource projects, or widely used internal tools.

  • Background with safety, reliability, and validation requirements for autonomous driving or other safetycritical applications.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June 13, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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