Senior Machine Learning Engineer - LLM Quantization & Deployment

Xpengmotors

Santa Clara, CA

On-siteCareer-pivot friendly

Who this role is best for

Candidates with expertise in LLM quantization and deployment will find this senior role in AI chip development at XPENG.

Best fit for

Senior engineers with production experience in LLM quantization techniques and strong Python skills
— “Develop production-quality Python code with strong testing, observability, reproducibility, and failure handling.”
Individuals who have worked on model export, calibration, and deployment pipelines for deep learning
— “Build robust model export, calibration, benchmarking, validation, and deployment pipelines.”
Candidates with a background in Transformer architectures and AI chip development for autonomous systems
— “Strong understanding of Transformer architectures and LLM inference.”

Things to consider

This role requires working with in-vehicle software and field-testing teams on a regular basis
— “Serve as the primary interface with field-testing and simulation teams for issue triage and autonomous driving performance sign-off.”
The position demands a balance between numerical error analysis and performance optimization
— “Analyze numerical errors, accuracy regressions, and performance trade-offs.”

How to stand out

Highlight experience with LLM runtimes like TensorRT-LLM or vLLM in your resume and interview responses
— “Experience with one or more LLM runtimes, such as TensorRT-LLM, vLLM, SGLang, llama.cpp, ONNX Runtime, TVM, MLIR, or custom runtimes.”
Demonstrate your ability to curate evaluation datasets and define performance metrics systematically
— “Curate evaluation datasets and establish a comprehensive metric suite to systematically benchmark VLA performance.”
Showcase projects involving quantization methods like AWQ or GPTQ in your portfolio or resume
— “Experience with AWQ, GPTQ, SmoothQuant, or related methods.”

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Developing robust model deployment pipelines
Curating evaluation datasets
Analyzing numerical errors and performance trade-offs

Typical background

Experience in machine learning and deep learningBackground in software engineering

Skills & requirements

Required

Machine LearningQuantization TechniquesModel DeploymentPython ProgrammingDeep Learning

Preferred

LLM Quantization MethodsModel OptimizationInference On Heterogeneous Hardware

Stack & domain

PythonPyTorchTensorrt-llmVllmONNX RuntimeTVMMLIRCommunicationProblem-solvingMachine LearningQuantizationDeployment

About the role

Original posting from Xpengmotors via Greenhouse

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.
Key Responsibilities
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.
Basic Qualifications
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.
Preferred Qualifications
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.
What We Provide
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.

Source: Xpengmotors careers (Greenhouse)