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
Algorithms
Bash @ 7
CI/CD @ 4
Communication @ 7
Computer Vision @ 7
Debugging
Deep Learning @ 7
Distributed Systems @ 4
Docker @ 4
GenAI
Generative AI
Jenkins @ 4
Linux @ 7
Machine Learning @ 4
Mathematics @ 4
Performance Optimization
PyTorch @ 7
Python @ 7
Statistics @ 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 seeking a highly motivated Software Engineer to join our Autonomous Vehicle (AV) Simulation team. In this role, you will help build and scale realistic virtual environments that accelerate the training, testing, and validation of NVIDIA’s autonomous driving software stack.
Our mission is to enable large-scale simulation and debugging of AV algorithms across millions of scenarios per day, spanning diverse traffic patterns, road conditions, weather environments, and edge cases. Achieving this requires deep performance optimization and systems-level analysis across the entire software stack — from AV algorithms and AI models to system software, infrastructure, and production-scale simulation workflows.
Responsibilities
- Develop scalable simulation platforms and workflows for autonomous driving validation and training.
- Work on Real2Sim and Sim2Real domain adaptation technologies to transform real-world driving incidents into diverse simulation scenarios — and bridge the gap between simulated and real-world behavior.
- Contribute across a multidisciplinary stack involving: System software and distributed infrastructure, neural graphics & rendering, generative AI & synthetic data generation, computer vision & deep learning, and real-to-synthetic domain adaptation.
- Optimize large-scale simulation workflows for performance, scalability, reliability, and production deployment.
- Collaborate closely with researchers, infrastructure engineers, and product teams across NVIDIA.
- Drive technology transfer into production products and contribute to open-source initiatives where applicable.
Requirements
- BS, MS, or PhD in Computer Science, Electrical Engineering, Artificial Intelligence, or a related field (or equivalent experience).
- 5+ years of relevant autonomous vehicles industry experience.
- Strong programming skills in Python, C/C++, PyTorch, and Linux/bash scripting.
- Experience with modern software engineering and infrastructure tools such as Docker, Bazel, Jenkins, CI/CD pipelines, and distributed systems tooling.
- Strong background in computer vision, deep learning, simulation systems, or related domains.
- Excellent analytical and mathematical problem-solving skills.
- Ability to independently drive complex projects from concept to production.
- Strong communication, collaboration, and teamwork skills.
- Experience in machine learning, large-scale systems, analytics, statistics, or applied mathematics.
Benefits
- You will also be eligible for equity and benefits.
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