2 months ago
Houston, TX, USA or San Francisco, CA, USAMid Level
Responsibilities
- Optimize end-to-end GPU performance for real-time autonomous driving workloads, including camera and LiDAR processing and neural network inference.
- Develop and optimize parallel computing algorithms and GPU-accelerated components using CUDA.
- Design and improve onboard GPU software architectures for perception, planning, and control modules.
- Profile and analyze bottlenecks in 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.
- Collaborate with software engineers, AI researchers, and hardware specialists on high-performance autonomous driving solutions.
Requirements
- 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 with 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 large sensor data streams from cameras, LiDAR, and radar.
- Strong proficiency in C/C++ and Python.
- Preferred: 3+ years of experience in GPU programming and optimization using technologies such as CUDA, OpenCL, or Vulkan.
- Preferred: Experience with NVIDIA Jetson Thor, NVIDIA DRIVE Thor, or similar embedded GPU platforms.
- Preferred: Experience with model quantization, including FP8 and NVFP4.
- Preferred: Experience managing concurrent GPU workloads and resource isolation using NVIDIA MPS, MIG, or related technologies.
- Preferred: Experience with GPU-accelerated sensor data compression.
Categories
About Bot Auto
Bot Auto develops and operates Level 4 autonomous trucks, selling freight capacity as Transportation-as-a-Service to shippers rather than licensing technology. The Houston-based, privately held company (founded 2023) owns the trucks and runs end-to-end operations, pairing fleet operators with autonomy engineers to deploy long-haul routes. Its platform spans autonomy software, vehicle controls, sensors, compute, and fleet operations with a documented safety case for commercial freight service.