Autonomous Driving GPU Engineer — Real-Time CUDA & ML

Bot-Auto; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Bot Auto is hiring an experienced GPU-focused engineer to advance autonomous driving workloads. You will optimize end-to-end GPU performance, including sensor processing (camera, LiDAR) and neural network inference, and collaborate with software and hardware teams to optimize onboard GPU software for perception, planning, and control subsystems.

The role emphasizes CUDA-based development, profiling, and deployment on embedded platforms, with opportunities to work across SF Bay Area or relocation

Qualifications

• Bachelor's or Master's degree in CS/EE or related field.
• Strong knowledge of parallel computing, GPU architecture and memory hierarchy.
• Experience profiling GPU apps with Nsight or similar tools.
• Experience deploying/optimizing neural network inference (PyTorch/ONNX/TensorRT).
• Experience with real-time embedded systems and sensor data streams.

Responsibilities

• Optimize end-to-end GPU performance for real-time autonomous driving workloads.
• Develop and optimize CUDA-based parallel computing algorithms.
• Design and improve onboard GPU software architectures for perception, planning, and control modules.
• Profile and analyze bottlenecks across GPU compute, memory, and CPU-GPU interaction.
• Debug and optimize GPU-based software to reduce latency and improve throughput on embedded platforms.

Skills

Parallel computing

GPU programming

C/C++

Python

Profiling GPU

Education

Bachelor's or Master's degree

Tools

CUDA

NVIDIA Nsight

TensorRT

PyTorch

ONNX

Job description

Bot Auto is hiring an experienced GPU-focused engineer to advance autonomous driving workloads. You will optimize end-to-end GPU performance, including sensor processing (camera, LiDAR) and neural network inference, and collaborate with software and hardware teams to optimize onboard GPU software for perception, planning, and control subsystems.

The role emphasizes CUDA-based development, profiling, and deployment on embedded platforms, with opportunities to work across SF Bay Area or relocation