S Senior Machine Learning Performance Engineer Santa Clara, CA, US Principal Python C++ PyTorch

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.

We are building the next generation of end-to-end autonomous driving on physical AI foundation models — vision-language-action policies and world models — and the same training stack carries our robotics program. These training runs span thousands of accelerators and days of wall-clock — how fast we train them is how fast we learn. As a Senior Staff Machine Learning Performance Engineer, you will own that speed: working with the modeling teams on their hardest bottlenecks, setting the technical direction for performance, and turning one-off wins into defaults — including the agentic workflows that find the next round of them automatically.

• Training Performance at Scale: Own step time, scaling efficiency and long-run stability for foundation-model training across thousands of accelerators — from the data path to kernels, parallelism and communication.
• Cross-Team Technical Leadership: Work directly with the modeling, infrastructure, simulation, data-mining and robotics teams on their hardest bottlenecks, and set the performance direction those teams build against.
• Reusable by Design: Turn one-off wins into modular components that become defaults across most models and workloads, rather than a patch that helps a single job.
• Observability at Scale: Extend performance tooling and metric coverage across the stack, while keeping instrumentation overhead low enough to stay on for production jobs fleet-wide.
• Agentic Automation: Build agents that reproduce any job on demand, verify numerical parity and separate a real regression from run-to-run noise — so that most of the optimizations that land are found and fixed by AI rather than by hand.

• Master's or PhD in CS, CE or EE, or equivalent practical experience, with 8+ years of industry experience, the majority of it in performance engineering.
• Hands-on experience optimizing GPU training or inference workloads in production, at multi-node scale.
• A track record of diagnosing performance problems in real production workloads and landing the fixes — often in systems owned by other teams — with the gain measured and verified in production.