You would collaborate with software engineers, AI researchers, and hardware specialists to develop high-performance solutions that meet the stringent requirements of autonomous driving applications. This is an exciting opportunity to work on next-generation transportation technology and make a meaningful impact on the future of mobility. Key Responsibilities • Optimize end-to-end GPU performance for real-time autonomous driving workloads, including sensor processing (e.g., camera, LiDAR) and neural network inference. • Develop and optimize parallel computing algorithms and GPU-accelerated components using technologies such as CUDA. • Collaborate with cross-functional teams to design and improve onboard GPU software architectures that meet the computational requirements of perception, planning, and control modules. • Profile and analyze bottlenecks across GPU computation, memory access, data movement, synchronization, and CPU-GPU interaction. • Debug and optimize GPU-based software to improve latency, throughput, resource utilization, and runtime stability on embedded platforms. Qualifications: Required: • Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field. • Strong knowledge of parallel computing principles, GPU architecture, memory hierarchy, and performance optimization techniques. • Experience profiling GPU applications using tools such as NVIDIA Nsight Systems, Nsight Compute, or equivalent tools. • Experience deploying or optimizing neural network inference workloads using technologies such as PyTorch, ONNX, and TensorRT. • Experience with real-time embedded systems and handling large data streams from sensors (camera, LiDAR, radar). • Strong proficiency in C/C++ and Python. Preferred: • 3+ years of experience in GPU programming and optimization (e.g., CUDA, OpenCL, Vulkan). • Experience with NVIDIA Jetson Thor, NVIDIA DRIVE Thor, or similar embedded GPU platforms. • Experience with model quantization, including FP8 and NVFP4. • Experience managing concurrent GPU workloads and resource isolation using technologies such as NVIDIA Multi-Process Service (MPS), Multi-Instance GPU (MIG), or other related technologies. • Experience with GPU-accelerated sensor data compression, including camera, LiDAR, or other onboard sensor data.