At Autobrains, we are building next-generation Level 4 autonomous driving vehicles. We are looking for an experienced software engineer with hands-on experience with NVIDIA DRIVE platforms to help bring up and optimize our software system on NVIDIA DRIVE Thor.

In this role, you will work with NVIDIA DRIVEWORKS, focusing on system bring-up, resource and memory management, and designing software architectures that efficiently leverage NVIDIA hardware acceleration (GPU, DLA, and other compute resources). You will collaborate with algorithm, hardware, and verification teams to ensure reliable, high-performance software running on real autonomous vehicles

Responsibilities:

• Bring up and integrate software systems on NVIDIA DRIVE platforms (Xavier / Orin / Thor).
• Develop and maintain platform-level software and infrastructure components.
• Work with NVIDIA DRIVEWORKS SDK to support perception and system pipelines.
• Manage system resources, memory, and compute utilization to ensure optimal performance.
• Design software architectures that leverage GPU, DLA, and heterogeneous compute resources.
• Debug, profile, and optimize system-level software for performance and reliability.
• Integrate software with sensors, hardware components, and other platform modules.
• Collaborate with cross-functional teams including algorithms, hardware, and verification.
• Support testing, validation, and debugging on development platforms and vehicles.
  
  

Requirements:

• B.Sc. or M.Sc. in Computer Science, Electrical Engineering, or a related field.
• 5+ years of software development experience in embedded or high-performance systems.
• Strong programming skills in C++ and Python.
• Hands-on experience with NVIDIA DRIVE platforms (Xavier / Orin / Thor) and DRIVEWORKS.
• Experience with Linux-based embedded systems.
• Strong understanding of memory management, concurrency, and system-level performance optimization.
• Strong debugging and problem-solving skills.
  
  

Advantages

• Experience with ROS / ROS2.
• Experience with CUDA or GPU-based acceleration.
• Experience deploying AI models on edge platforms (TensorRT, ONNX Runtime).
• Experience with camera, LiDAR, or IMU integration.
• Background in ADAS or autonomous driving systems.