Used Tools & Technologies
Not specified
Required Skills & Competences
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.
Linux @ 4
Python @ 4
Algorithms @ 4
Communication @ 7
Debugging @ 4
CUDA @ 4
GPU @ 4
AI @ 4
Profiling @ 4
Robotics @ 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 the company is focusing on the next era of computing powered by AI. This role contributes to GPU-accelerated simulation and robotics algorithms with NVIDIA Warp and works on deploying Warp-based robotics and simulation components onto embedded platforms.
Responsibilities
- Own and improve the end-to-end path for deploying Warp-based robotics and simulation components onto embedded platforms such as Jetson.
- Build and maintain reproducible deployment workflows including cross-compilation, CI, packaging, and containerized delivery for embedded robotics targets.
- Optimize on-device performance under real constraints including latency, throughput, memory footprint, thermals, and power.
- Debug complex issues across the stack spanning Python, C++, CUDA, drivers, and embedded Linux, including hard-to-reproduce device-specific failures.
- Integrate Warp components into robotics applications and frameworks, including ROS 2 and Isaac-based stacks, and work with partner teams to unblock adoption.
- Develop system-level testing, validation, and performance regression infrastructure for embedded targets.
- Collaborate with compiler, runtime, and kernel engineers to improve portability and performance across GPU architectures and embedded configurations.
Requirements
- B.Sc., M.Sc., Ph.D. or equivalent experience in Computer Science, Computer Engineering, Robotics, Applied Math, Physics, or a related field.
- 8+ years of software engineering experience with C++ and Python; comfortable working across build systems and deployment tooling.
- Experience shipping software to embedded or edge devices, ideally in robotics, autonomy, or real-time systems.
- Practical understanding of Linux-based deployment workflows including packaging, dependencies, drivers, and debugging in constrained environments.
- Ability to reason about GPU performance and memory behavior, and to diagnose bottlenecks using profiling and system tools.
- Strong collaboration and communication skills, with a bias toward execution and unblocking users.
Compensation
- Base salary range for Level 4: 184,000 USD - 287,500 USD
- Base salary range for Level 5: 224,000 USD - 356,500 USD
- Eligible for equity and benefits.
Additional information
- Applications accepted at least until February 28, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and is committed to fostering a diverse work environment.