GPU Engineer
Botauto · Houston, TX or San Francisco Bay Area
About this role
**GPU Engineer** **Company Introduction** At Bot Auto, we’re revolutionizing the transportation of goods with cutting-edge autonomous trucks—enhancing quality of life for communities worldwide. With the agility of a startup and the expertise of seasoned professionals, our team has delivered numerous world-firsts and breakthrough innovations. Join us and help turn bold ideas into real-world impact in the future of mobility. You’ll collaborate with software engineers, AI researchers, and hardware specialists to build high-performance solutions for autonomous driving—meeting the demanding requirements of real-time systems. **Key Responsibilities** - Optimize end-to-end GPU performance for real-time autonomous driving workloads, including sensor processing (camera, LiDAR) and neural network inference. - Develop and optimize parallel computing algorithms and GPU-accelerated components using technologies such as CUDA. - Collaborate cross-functionally to design and improve onboard GPU software architectures for 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. - Experience profiling GPU applications using NVIDIA Nsight Systems, Nsight Compute, or equivalent tools. - Experience deploying or optimizing neural network inference workloads using PyTorch, ONNX, and TensorRT. - Experience with real-time embedded systems and handling large sensor data streams (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 NVIDIA Multi-Process Service (MPS), Multi-Instance GPU (MIG), or similar technologies. - Experience with GPU-accelerated sensor data compression (camera, LiDAR, or other onboard sensors).
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