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Staff Machine Learning Engineer – Autonomous Driving Model Quantization & Deployment

Staff Machine Learning Engineer – Autonomous Driving Model Quantization & Deployment

Posted 4 hours ago

Job Description

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.

The Mission: The challenge of Vision-Language-Action (VLA) models and Foundation Models isn't just their intelligence—it's their real-time execution at the edge. We are seeking a high-caliber Staff Machine Learning Engineer to bridge the gap between massive research models and production-ready L4 autonomous driving systems. You will lead the effort to optimize and deploy our VLA models onto vehicle-grade compute platforms for our global fleet.

Key Responsibilities:

Lead Optimization Strategy: Own the end-to-end quantization and optimization roadmap for large-scale multimodal models (Transformers, VLMs).
Model Compression: Apply and innovate in PTQ (Post-Training Quantization), QAT (Quantization-Aware Training), and pruning techniques to fit VLA models into strict memory and power envelopes.
Hardware-Software Co-design: Collaborate directly with model researchers to ensure architectures are "deployment-friendly" and with platform teams to influence future hardware requirements.
Production Excellence: Develop and maintain robust, safety-critical deployment stacks in Modern C++, ensuring 24/7 stability and deterministic performance on the road.

Basic Qualifications:

Proven Track Record: 5-8 years of experience in model deployment, quantization, or high-performance computing (HPC).
Core Technical Skills: Mastery of Modern C++ and deep experience with CUDA or other hardware acceleration libraries.
Deep Learning Expertise: Strong familiarity with PyTorch and deep knowledge of inference engines like TensorRT, ONNX Runtime, or TVM.
Quantization Depth: Hands-on experience with INT8/FP8/INT4 quantization and knowledge of the unique challenges in quantizing Large Language Models (LLMs) or Transformers.
Platform Knowledge: Solid understanding of computer architecture (Cache, Memory Bandwidth, SIMD) and experience with embedded/edge compute constraints.
Systems Thinking: Ability to debug complex performance bottlenecks across the entire software stack.

Preferred Qualifications:

Experience with VLA/VLM or other Foundation Model deployment.
Background in autonomous driving, robotics, or real-time safety-critical systems.
Contributions to open-source inference or compiler projects.

What do we provide:

A fun, supportive and engaging environment
Infrastructures and computational resources to support your ML model development/research.
Opportunity to work on cutting edge technologies with the top talent in the field.
Opportunity to make significant impact on 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.

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