Staff Machine Learning Engineer - AI Foundation at XPENG — CAN | Base Career

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Optimize transformer-based LLMs for low-latency and high-throughput inference.
Optimize kernels and model graphs using tools like CUDA, Triton, and custom fused operators.
Implement and benchmark (Quantization, Knowledge distillation, structured and unstructured pruning, KV-cache optimization, etc.).
Deploy optimized models across GPUs, CPUs, and edge acceleators.
Contribute to internal tooling and documentation for model optimization flows.
Master in CS/CE/EE, or equivalent, with 5-8 years of industry experience.
Good knowledge of PyTorch.
Knowledge of transformer architecture and ways to accelerate the training and inference of transformer models.
Previous experience in the autonomous driving industry.
Knowledge of Torchscript and Nvidia TensorRT.
Strong programming skills in Python and C++
Familiarity with GPU CPU, NPU, DSP architecture.
Deep understanding of memory bandwidth, compute bottlenecks, and hardware-aware model optimization
Being efficiently in solving complex problems collaboratively on larger teams
A fun, supportive and engaging environment.
Infrastructures and computational resources to support your work.
Opportunity to work on cutting edge technologies with the top talents in the field.
Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving.
Competitive compensation package.
Snacks, lunches, dinners, and fun activities.

Staff Machine Learning Engineer - AI Foundation