The Role
Lead development and productionization of LLM quantization and deployment pipelines (PTQ, QAT, mixed-precision). Implement robust Python production code, build export/calibration/benchmarking workflows, collaborate across research, field-testing, vehicle software, and training infrastructure teams, and curate evaluation datasets and metrics to validate model accuracy, latency, and numerical consistency for on-vehicle inference.
Summary Generated by Built In
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.
Our mission is to build strong foundation for LLM deployment and quality sign-off for next-gen XPENG Turing AI chip. This includes and is not limited to: LLM model fine tuning, PTQ, QAT, on-vehicle inference and related fields.
- Develop VLA inference models, ensure numerical consistency with training models, and productionize LLM quantization methods, including PTQ, QAT, mixed-precision inference, INT8, FP4, and lower-bit techniques.
- Develop production-quality Python code with strong testing, observability, reproducibility, and failure handling.
- Build robust model export, calibration, benchmarking, validation, and deployment pipelines.
- Engage early with the VLA model research team to establish performance estimates and prove model feasibility.
- Curate evaluation datasets and establish a comprehensive metric suite to systematically benchmark VLA performance.
- Analyze numerical errors, accuracy regressions, and performance trade-offs.
- Develop PTQ and QAT orchestration workflows.
- Serve as the primary interface with field-testing and simulation teams for issue triage and autonomous driving performance sign-off.
- Collaborate with the in-vehicle software team on latency analysis and issue triage.
- Collaborate with the training infrastructure team to develop QAT and model distillation.
- Master in CS/CE/EE, or equivalent, with 1-3 years of industry experience. Open to new graduates.
- Strong understanding of Transformer architectures and LLM inference.
- Hands-on experience quantizing or deploying deep learning models in production.
- Proficiency with PyTorch and at least one inference or compilation stack.
- Strong Python programming and software engineering skills.
- Ability to work effectively across research, systems, infrastructure, and product teams.
- Excellent communication and problem-solving skills, with the ability to thrive in a fast-paced and collaborative environment.
- Experience with weight-only, activation, KV-cache, dynamic, static, or mixed-precision quantization.
- Experience with AWQ, GPTQ, SmoothQuant, or related methods.
- Strong numerical analysis and systems engineering skills.
- Experience with one or more LLM runtimes, such as TensorRT-LLM, vLLM, SGLang, llama.cpp, ONNX Runtime, TVM, MLIR, or custom runtimes.
- Experience deploying LLMs on resource-constrained or heterogeneous hardware.
- Contributions to model optimization, inference, compiler, or serving projects.
- Publications at NeurIPS, ICML, ICLR, ACL, or related conferences.
- 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 a significant impact on the transportation revolution by the means of advancing autonomous driving.
- Competitive compensation package.
- Snacks, lunches, dinners, and fun activities.
The base salary range for this full-time position is $174,720 - $295,680, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.
Skills Required
- Master in CS/CE/EE or equivalent (1-3 years industry experience; open to new graduates)
- Strong understanding of Transformer architectures and LLM inference
- Hands-on experience quantizing or deploying deep learning models in production
- Proficiency with PyTorch
- Experience with at least one inference or compilation stack
- Strong Python programming and software engineering skills (production-quality code, testing, observability)
- Ability to work effectively across research, systems, infrastructure, and product teams
- Excellent communication and problem-solving skills
- Experience with weight-only, activation, KV-cache, dynamic/static/mixed-precision quantization
- Experience with AWQ, GPTQ, SmoothQuant or related quantization methods
- Experience with LLM runtimes (TensorRT-LLM, vLLM, SGLang, llama.cpp, ONNX Runtime, TVM, MLIR, or custom runtimes)
- Experience deploying LLMs on resource-constrained or heterogeneous hardware
- Strong numerical analysis and systems engineering skills
- Contributions to model optimization, inference, compiler, or serving projects or related publications
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The Company
What We Do
Xpeng Motors is a leading Chinese electric vehicle and technology company that designs and manufactures intelligent automobiles that are seamlessly integrated with the Internet and utilize the latest advances in artificial intelligence. Focusing on China’s young and tech-savvy consumer base, XPENG Motors strives to offer smart mobility solutions with technology innovation and cutting-edge R&D. The company’s initial backers include its CEO & Chairman He Xiaopeng, the founder of UCWeb Inc. and a former Alibaba executive. It was co-founded in 2014 by Henry Xia and He Tao, former senior executives at Guangzhou Auto with expertise in innovative automotive technology and R&D. It has received funding from prominent Chinese and international investors including Alibaba Group, Foxconn Group and IDG Capital. Currently with 3,000 employees, the company is headquartered in Guangzhou and has design, R&D, manufacturing and sales & marketing divisions in Silicon Valley, San Diego, Beijing, Shanghai, Zhaoqing (Guangdong Province) and Zhengzhou (Henan Province).
