Staff Machine Learning Engineer - LLM Quantization & Deployment

Xpengmotors

Santa Clara, CA

On-site

Who this role is best for

Aimed at mid-level machine learning engineers with experience in LLM quantization and deployment, who can work collaboratively across teams and have a strong technical foundation in AI and autonomous driving technologies.

Best fit for

Mid-level ML engineers with production deployment experience in LLM quantization and strong Python skills
— “Develop production-quality Python code with strong testing, observability, reproducibility, and failure handling.”
Candidates with background in Transformer architectures and quantization methods such as PTQ and QAT
— “Strong understanding of Transformer architectures and LLM inference.”
Individuals who can bridge research and systems teams with a focus on model feasibility and performance
— “Engage early with the VLA model research team to establish performance estimates and prove model feasibility.”

Things to consider

This role requires working with in-vehicle software and simulation teams, which may involve cross-functional coordination and hardware-specific considerations.
— “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 analysis and systems engineering, which could be a challenge for those with only one of these skill sets.
— “Strong numerical analysis and systems engineering skills.”

How to stand out

Highlight experience with quantization methods like AWQ or GPTQ in your resume and interview responses.
— “Experience with AWQ, GPTQ, SmoothQuant, or related methods.”
Showcase your ability to build robust model pipelines and handle deployment challenges in your project examples.
— “Build robust model export, calibration, benchmarking, validation, and deployment pipelines.”
Emphasize collaboration with multiple teams and your ability to translate research into production-ready solutions.
— “Ability to work effectively across research, systems, infrastructure, and product teams.”

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Mid Level

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

What success looks like

developed production-quality Python code
curated evaluation datasets
established comprehensive metric suite

Typical background

Master in CS/CE/EE3-5 years of industry experience

Skills & requirements

Required

LLM QuantizationModel Fine TuningPTQQATOn-vehicle Inference

Preferred

Mixed-precision InferenceINT8FP4Lower-bit Techniques

Stack & domain

Transformer ArchitecturesLLM InferencePyTorchPythonPTQQATMixed-precision InferenceINT8FP4Lower-bit TechniquesCommunicationProblem-solvingAIMachine LearningAutonomous DrivingSmart Connectivity

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 3-5 years of industry experience.
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 $215,280 - $364,320, 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)