Senior AI Inference & Optimization Engineer — Autonomy

DiDi Labs; San Jose (CA); On-site; USD 170,000 - 339,000; Full time

Job summary

DiDi Labs is seeking an experienced Senior/Staff AI Infrastructure Engineer, Inference & Optimization to lead performance tuning and deployment of AI models across vehicle and cloud infrastructure. You will design high‑efficiency inference pipelines and system stability frameworks for ultra‑low latency operation in autonomous systems.

Collaborate with perception, cloud, and safety teams to accelerate model iteration, implement profiling, and optimise hardware usage on NVIDIA GPUs across Hopper

Qualifications

• Bachelor’s or higher degree in Computer Science, Software Engineering, Systems Engineering, or a closely related field.
• 3–8+ years of industry experience in high‑performance computing, AI infrastructure, model optimisation, or embedded deployment.
• Strong proficiency in C++ and Python, with expertise in parallel programming (CUDA, OpenMP) and low‑level system profiling tools.
• Deep familiarity with inference engines (TensorRT, ONNX Runtime) and specialised LLM inference/serving frameworks (vLLM, SGLang, TensorRT‑LLM).
• Practical understanding of modern GPU hardware architectures (e.g., NVIDIA Hopper, Thor) and memory bandwidth management.
• Demonstrated ability to diagnose complex software‑hardware integration issues and drive scalable, production‑grade solutions.

Responsibilities

• Own the deployment, optimisation, 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 optimisation toolchains, quantisation techniques, and execution engines.
• Establish system‑level profiling and telemetry frameworks using CUDA tools to monitor, analyse, and maximise hardware utilisation 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

GPU profiling

Root-cause analysis

Education

Bachelor's degree or higher in CS/Engineering

Tools

TensorRT

ONNX Runtime

vLLM

SGLang

TensorRT-LLM

CUDA tools

Job description

DiDi Labs is seeking an experienced Senior/Staff AI Infrastructure Engineer, Inference & Optimization to lead performance tuning and deployment of AI models across vehicle and cloud infrastructure. You will design high‑efficiency inference pipelines and system stability frameworks for ultra‑low latency operation in autonomous systems.

Collaborate with perception, cloud, and safety teams to accelerate model iteration, implement profiling, and optimise hardware usage on NVIDIA GPUs across Hopper