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
Machine Learning
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 is building the next generation AI-native autonomous driving architecture — combining classical safety stacks, foundation models, and scalable AI systems into a unified production platform.
As part of NVIDIA’s Autonomous Driving division, you will work on systems that build the future of autonomous systems over the next decade.
Responsibilities
- Compose and build the architecture behind next-generation self-driving vehicle technology.
- Work on Prediction, Decision, Planning and Control architecture.
- Have exposure to Classical safety stack.
- Build robust system-level safety and fallback strategies.
- Work on End-to-end data-driven AV pipelines.
- Hands on experience in DVLA / VA driving models.
- Develop a World Model–based planning and reasoning model.
- Have the exposure to engage with Large-scale model inference architecture.
- Contribute to the integration of innovative research in robotics into self-driving vehicle technologies.
- Develop deep knowledge of end-to-end AV software integration from perception through control, including dependencies, interface management, and performance tuning.
Requirements
- 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.
- Production experience in autonomous driving systems.
- Worked on AI foundation models or large-scale ML systems.
- Helped drive end-to-end driving models.
- Experience working on Robotics or embodied AI systems.
- Knowledge and experience in system architecture from 0 → 1 → scale.
Ways To Stand Out From The Crowd
- PhD in a relevant field or related research experience.
- Knowledge of CUDA is a plus.
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