Senior AI Infrastructure Engineer - Real-Time Inference

Didi Labs; San Jose (CA); Hybrid; USD 170,000 - 351,000; Full time

Job summary

DiDi seeks a Senior/Sr. Staff AI Infrastructure Engineer, Inference & Optimization in San Jose, CA, to lead performance tuning, deployment, and resource scheduling of AI models across vehicle and cloud infrastructure.

You will design high‑efficiency inference pipelines, build system stability frameworks, and optimize hardware execution for ultra‑low latency, collaborating with perception/prediction, cloud, and safety teams to enable rapid model iteration and scalable deployment.

Qualifications

• 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.
• Strong proficiency in C++ and Python, with solid expertise in parallel programming (CUDA, OpenMP) and low‑level system profiling tools.
• Deep familiarity with mainstream inference engines (e.g., TensorRT, ONNX Runtime) and specialized LLM inference/serving frameworks (e.g., vLLM, SGLang, TensorRT‑LLM).

Responsibilities

• 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.
• 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.

Skills

C++

Python

CUDA

OpenMP

System profiling tools

Education

Master's degree in CS/SE/related field

Tools

TensorRT

ONNX Runtime

vLLM

SGLang

TensorRT-LLM

Job description

DiDi seeks a Senior/Sr. Staff AI Infrastructure Engineer, Inference & Optimization in San Jose, CA, to lead performance tuning, deployment, and resource scheduling of AI models across vehicle and cloud infrastructure.

You will design high‑efficiency inference pipelines, build system stability frameworks, and optimize hardware execution for ultra‑low latency, collaborating with perception/prediction, cloud, and safety teams to enable rapid model iteration and scalable deployment.