Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
CUDA @ 4
GPU
Machine Learning @ 4
Robotics @ 4
System Architecture @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. Today, NVIDIA is using AI to define the next era of computing, with GPUs powering computers, robots, and self-driving cars.
NVIDIA is building the next generation of AI-native autonomous driving architecture by combining classical safety stacks, foundation models, and scalable AI systems into a unified production platform. The next generation of autonomous vehicle systems will integrate classical safety architectures with large-scale AI-driven systems.
Responsibilities
- Compose and build the architecture behind next-generation self-driving vehicle technology.
- Work on prediction, decision, planning, and control architecture.
- Gain exposure to classical safety stacks.
- Build robust system-level safety and fallback strategies.
- Work on end-to-end, data-driven autonomous vehicle pipelines.
- Gain hands-on experience with DVLA and VA driving models.
- Develop a world model-based planning and reasoning model.
- Work with large-scale model inference architecture.
- Contribute to the integration of innovative robotics research into self-driving vehicle technologies.
- Integrate end-to-end autonomous vehicle software from perception through control, including dependencies, interface management, and performance tuning.
Requirements
- PhD with 4+ years of experience, MS with 6+ years of experience, or BS or equivalent experience with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
- Production experience in autonomous driving systems.
- Experience working on AI foundation models or large-scale machine learning systems.
- Experience helping drive end-to-end driving models.
- Experience working on robotics or embodied AI systems.
- Knowledge and experience with system architecture from 0 to 1 and through scale.
- Ability to build systems, not just individual components.
Preferred Qualifications
- PhD in a relevant field or related research experience.
- Knowledge of CUDA.
Compensation And Benefits
The base salary range is $184,000-$287,500 USD for Level 4 and $224,000-$356,500 USD for Level 5. Salary is determined based on location, experience, and the pay of employees in similar positions. The role is also eligible for equity and benefits.
NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer. Applications will be accepted at least until July 27, 2026.