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AI Intern – VLA Deployment

XPENG · Santa Clara, CA

General Intelligence Center SV Office Posted May 5, 2026

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: Vision-Language-Action (VLA) models and foundation models are becoming increasingly important in autonomous driving, but turning research models into real-time, production-ready systems on vehicle hardware remains a major challenge. We are looking for an entry-level engineer or intern to support the optimization and deployment of multimodal models onto vehicle-grade compute platforms. This role is a strong fit for candidates who are excited about deep learning systems, model deployment, and edge inference for real-world autonomous driving applications.

Key Responsibilities

Support model quantization and deployment efforts for large-scale multimodal models, including Transformers and vision-language models.
Assist with applying model optimization techniques such as post-training quantization, quantization-aware training, pruning, and related compression methods under guidance from senior engineers.
Work with research and platform teams to help improve model deployability and understand hardware and runtime constraints.
Contribute to deployment tools, test pipelines, and runtime modules in C++ and Python for autonomous driving systems.
Help analyze model performance, memory usage, latency, and numerical accuracy across different deployment targets.
Participate in debugging and performance tuning across the model, runtime, and system stack.
Support validation and testing workflows to ensure stable and reliable deployment in vehicle and simulation environments.

Basic Qualifications

BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field.
Strong programming skills in C++ and/or Python.
Familiarity with deep learning frameworks such as PyTorch.
Basic understanding of model inference, deployment, or optimization workflows using tools such as ONNX, TensorRT, or similar frameworks.
Exposure to model compression or quantization concepts such as INT8, FP16, or related approaches.
Interest in computer architecture, performance optimization, and edge or embedded systems.
Strong problem-solving skills and the ability to learn quickly in a fast-paced engineering environment.
Good communication skills and the ability to collaborate with cross-functional teams.

Preferred Qualifications

Internship, research, or project experience in deep learning model deployment, inference acceleration, or embedded AI.
Familiarity with Transformers, multimodal models, or foundation models.
Experience with CUDA or GPU programming.
Exposure to autonomous driving, robotics, or real-time systems.
Contributions to research projects, open-source repositories, or relevant course projects.

What do 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 significant impact on the transportation revolution by the means of advancing autonomous driving.
Competitive compensation package.
Snacks, lunches, dinners, and fun activities.

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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AI Intern – Scenario Analysis

XPENG · Santa Clara, CA

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General Intelligence Center SV Office Posted May 5, 2026

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 team is working on the deployment of next-generation Vision-Language-Action (VLA) models for global autonomous driving products, and we’re looking for an intern to support data analysis, vector search, scenario classification, and long-tail scenario mining.

This role will focus on helping us identify and understand global region-specific driving scenarios, especially long-tail cases that matter for real-world model performance and deployment.

Key Responsibilities

Analyze large-scale global driving data to identify long-tail and region-specific scenarios.
Build workflows for vector search / semantic retrieval over scenario-related data.
Apply ML and LLM-based methods for scenario classification, clustering, and tagging.
Use embedding models, prompt engineering, and RAG-style pipelines to organize unstructured data and surface useful patterns.
Support data mining, data cleaning, and scenario curation for VLA model improvement.
Generate analyses, dashboards, and summaries to help the team understand failure cases and regional data gaps.

Qualifications

Strong skills in Python, SQL, and data analysis.
Experience with machine learning pipelines, clustering, classification, or unsupervised learning.
Familiarity with embedding models, vector search, semantic retrieval, RAG, or prompt engineering.
Strong analytical thinking and comfort working with large datasets.
Interest in autonomous driving, multimodal ML, embodied AI, or VLA model.

What do 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 significant impact on the transportation revolution by the means of advancing autonomous driving.
Competitive compensation package.
Snacks, lunches, dinners, and fun activities.

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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Senior Staff AI Engineer

XPENG · Santa Clara, CA

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General Intelligence Center SV Office Posted May 5, 2026

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 looking for a hands-on Senior Staff AI Engineer to build and scale production-grade AI systems that drive measurable impact across internal teams. This role owns the end-to-end delivery of applied AI solutions—partnering with product, engineering, and cross-functional teams across the organization to identify high-value use cases, and translating them into scalable systems using LLMs, retrieval, and agentic workflows. You will also define architecture and reusable patterns to enable AI adoption broadly, while setting a high bar for reliability, evaluation, and performance.

Job Responsibilities:

Lead the technical design and hands-on development of prioritized AI applications, services, and platforms leveraging state-of-the-art LLM app stacks, retrieval-augmented generation, evaluation frameworks, and scalable serving.
Define long-term architecture and engineering standards for Applied AI systems to maximize reuse, reliability, and impact across multiple product areas.
Partner with business sponsors to translate high-value opportunities into roadmaps and shipped products with clear success metrics and measurable outcomes.
Build a holistic view of AI investments by collaborating with adjacent engineering groups implementing AI in their domains, aligning patterns, reusing components, and avoiding duplication.
Drive continuous improvement in AI methodologies and best practices; evaluate emerging capabilities and land them as secure, production-grade systems.
Collaborate with stakeholders to embed robust governance, privacy, security, safety, and reporting practices across the AI lifecycle.
Champion AI literacy, enablement, and adoption through demos, guidance, and technical leadership across the organization.
Establish rigorous evaluation, guardrails, and monitoring practices; instrument offline and online metrics to ensure quality, safety, and SLOs.

Minimum Skill Requirements:

BS/MS/PhD in Computer Science or a related field, or equivalent experience.
10+ years of software engineering experience, with a proven track record delivering complex, production-grade systems.
Deep technical expertise in AI/ML, with hands-on experience building and deploying systems using language models, retrieval/grounding (RAG), embeddings/vector search, and evaluation frameworks.
Hands-on experience building and scaling agentic AI systems in production environments.
Strong experience designing and implementing evaluation systems, including LLM-as-Judge frameworks, metric design, synthetic data generation, and agent benchmarking pipelines.
Proven ability to establish feedback loops from production systems into evaluation and model improvement workflows.
Expertise in AI/LLM observability and tracing, including instrumentation of systems and analysis of trace data for performance, latency, and correctness (e.g., OpenTelemetry-based tools such as LangFuse or equivalent).
Experience architecting and deploying enterprise-scale AI systems or subsystems, with the ability to define technical direction, architecture, and reusable platform components across teams.
Deep expertise in at least one of the following areas:
- Agent memory systems (context management, long-term persistence, retrieval optimization).
- AI gateways / tool orchestration layers (e.g., MCP, service integration, authorization, tool discovery).
- Agentic workflows and orchestration (multi-step planning, tool calling, error recovery, concurrency).
Demonstrated ability to translate ambiguous problems into scalable AI systems with measurable impact.
Experience driving engineering standards, improving system reliability, observability, and performance (latency, throughput, cost).
Strong familiarity with security, privacy, compliance, safety, and auditability in enterprise AI systems.
Excellent communication and cross-functional collaboration skills; able to influence and align across engineering, product, and other internal teams.
Proven ability to learn and apply new technologies quickly through hands-on development.

Preferred Skill Requirements:

Experience building custom evaluation harnesses or contributing to open-source harness frameworks.
Contributions to widely used third-party benchmarks or evaluation suites.
Hands-on experience developing and evaluating coding agents or code-generation systems.
Experience working with and deploying open-source language models.

What do we provide:

A fun, supportive and engaging environment.
Opportunity to make significant impact on transportation revolution by the means of advancing autonomous driving.
Opportunity to work on cutting edge technologies with the top talent in the field.
Competitive compensation package.
Snacks, lunches and fun activities.

The base salary range for this full-time position is $244,140 - $413,160, 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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Senior Staff Machine Learning Engineer – Autonomous Driving Foundation Models

XPENG · Santa Clara, CA

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General Intelligence Center SV Office Posted Apr 29, 2026

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: We are building the next generation of L4 autonomous vehicles. Moving beyond traditional modular stacks, we are developing large-scale Vision-Language-Action (VLA) models and World Models to handle the infinite long-tail scenarios of global driving. As a Senior Staff Machine Learning Engineer, you will architect the transition from behavior cloning to intelligent, zero-shot decision-making in diverse global markets.

Key Responsibilities:

Architectural Leadership: Lead the design of end-to-end VLA architectures, bridging multi-modal perception with high-level linguistic reasoning and precise action generation.
World Model Development: Drive R&D in generative world models (latent dynamics) to create high-fidelity, controllable driving simulations for closed-loop training and evaluation.
Policy Evolution: Apply Advanced RL (Online/Offline) and IL to refine driving policies, focusing on long-horizon planning and complex multi-agent interactions.
Scaling & Data Strategy: Define scaling laws for driving foundation models, overseeing data curation, automated labeling, and post-training at a multi-billion parameter scale.
Global Generalization: Lead the model’s adaptation strategy for overseas road conditions, ensuring robust performance across varying traffic laws and driving cultures.

Qualifications:

5-8 years of expertise in Deep Learning, with a significant track record in VLM, VLA, or Embodied AI.
Proven experience in training and deploying Foundation Models (Transformers, LLMs) at scale.
Deep understanding of Sequential Decision Making, World Models, or Policy Gradient methods.
Mastery of PyTorch and expertise in distributed training (DeepSpeed, Megatron, etc.).
A "Product-First" mindset: The ability to balance cutting-edge research with the deterministic requirements of L4 production vehicles.

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 $244,140-$413,160, 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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Senior Computer Vision Engineer

XPENG · Santa Clara, CA

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General Intelligence Center SV Office Posted Apr 14, 2026

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 seeking a talented Computer Vision / Machine Learning Engineer to join our global team. In this role, you will develop and optimize multi-modal models and computer vision systems, driving performance, efficiency, and real-world deployment. The ideal candidate has hands-on experience with multi-modal model training and optimization, a strong foundation in computer vision, and solid C++ engineering skills.

Key Responsibilities

Research and implement multi-modal large models (image-text, image-audio, etc.) training, fine-tuning, and inference optimization strategies, continuously improving model performance, efficiency, and generalization ability.
Design and optimize computer vision models and algorithms (e.g., detection, classification, segmentation, feature extraction) to support real-world applications.
Collaborate with cross-functional teams (product, engineering, data) to translate research into scalable, reliable, and production-ready solutions.
Use C++ to implement and optimize models and systems, including deployment, performance tuning, and integration, ensuring low latency and high throughput.
Stay up to date with advances in computer vision and multi-modal AI, and apply new methods to improve model performance and product impact.
Contribute to technical discussions, code reviews, and knowledge sharing to improve code quality and engineering best practices.

Minimum Qualifications

Master’s or Ph.D. in Computer Science or a related field, with strong expertise in computer vision and machine learning.
1-3 years of experience in multi-modal large model training, fine-tuning, and optimization (e.g., CLIP, Flamingo, BLIP, or self-developed multi-modal models), with a deep understanding of multi-modal fusion mechanisms.
Strong foundation in computer vision, including object detection, image classification, feature matching, and image enhancement.
Strong C++ development skills, with proficiency in STL, multi-threading, memory management, and performance optimization; experience in production-level implementation and deployment is required.
Familiar with deep learning frameworks (e.g., PyTorch, TensorFlow) and computer vision libraries (e.g., OpenCV, OpenMMLab).
Strong problem-solving ability, self-driven, and passionate about technological innovation; ability to work independently and in a team.

Preferred Qualifications

Experience in edge device algorithm deployment, published papers in top computer vision conferences (CVPR, ICCV, ECCV), or open-source project contributions in related fields.

What do we provide:

A fun, supportive and engaging environment.
Opportunity to make a significant impact on the transportation revolution by the means of advancing autonomous driving.
Opportunity to work on cutting edge technologies with the top talent in the field.
Competitive compensation package.
Snacks, lunches 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.

Ready to apply?

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Staff Machine Learning Engineer – Autonomous Driving Model Quantization & Deployment

XPENG · Santa Clara, CA

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General Intelligence Center SV Office Posted Apr 14, 2026

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.

Ready to apply?

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