Senior/Sr. Staff AI Infrastructure Engineer, Inference & Optimization

We are seeking an experienced and mission-driven Senior/Sr. Staff AI Infrastructure Engineer , Inference & Optimization to lead the performance tuning, deployment, and resource scheduling of cutting-edge AI models across on-vehicle and cloud infrastructure. In this role, you will design high-efficiency inference pipelines, build system-level stability frameworks, and optimize hardware execution to ensure ultra-low latency and rock-solid operational reliability. You will act as a technical leader in AI infrastructure, accelerating model iteration and bridging the gap between frontier deep learning algorithms and real-time autonomous systems.

• Own the deployment, optimization, and resource scheduling of vehicle-side AI models, ensuring high efficiency, low latency, and robust execution within embedded constraints.

• Lead vehicle-side system stability initiatives, conducting independent root-cause analysis and driving resolution for complex, system-level performance bottlenecks and runtime anomalies.

• Architect and scale service-oriented deployment environments for Large Language Models (LLMs) and foundational models to support offline simulation, automated annotation, and rapid model validation.

• Track and evaluate cutting-edge industry methodologies, continuously integrating advanced optimization toolchains, quantization techniques, and execution engines.

• Establish system-level profiling and telemetry frameworks using CUDA tools to monitor, analyze, and maximize hardware utilization across target GPU architectures.

• Collaborate cross-functionally with Autonomous Driving Perception/Prediction, Cloud Infrastructure, and Safety teams to enable rapid algorithm iteration and scalable vehicle deployment.

• Master’s or higher degree in Computer Science, Software Engineering, Systems Engineering, or a closely related technical field.

• 3-8+ years of industry experience in high-performance computing, AI infrastructure, model optimization, or embedded deployment.

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