Company
XPENG
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.
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Offices (2)
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6
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1
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6
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Senior Staff Machine Learning Engineer - Foundation Model
XPENG
Santa Clara, CA
AI Team
1w ago
Senior Staff Machine Learning Engineer – End-to-End Autonomous Driving
XPENG
Santa Clara, CA
AI Team
3w ago
Staff Machine Learning Engineer - Foundation Model
XPENG
Santa Clara, CA
AI Team
3w ago
Senior Machine Learning Engineer – Perception/ End-to-End
XPENG
Santa Clara, CA
AI Team
3w ago
Research Scientist Intern
XPENG
Santa Clara, CA
AI Team
3w ago
Senior Machine Learning Engineer - Foundation Model
XPENG
Santa Clara, CA
AI Team
3w ago
Pick a job to read the details
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Senior Staff Machine Learning Engineer - Foundation Model
Share this job
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 full-time Machine Learning Engineer / Research Scientist to drive the modeling and algorithmic development of XPENG’s next-generation Vision-Language-Action (VLA) Foundation Model — the core brain that powers our end-to-end autonomous driving systems.
You will work closely with world-class researchers, perception and planning engineers, and infrastructure experts to design, train, and deploy large-scale multi-modal models that unify vision, language, and control. Your work will directly shape the intelligence that enables XPENG’s future L3/L4 autonomous driving products.
Key Responsibilities
-
Design and implement large-scale multi-modal architectures (e.g., vision–language–action transformers) for end-to-end autonomous driving.
-
Develop pretraining and fine-tuning strategies leveraging massive labeled and unlabeled fleet data (images, video, LiDAR, CAN bus, maps, human driving behaviors, etc.).
-
Research and integrate cross-modal alignment (e.g., visual grounding, temporal reasoning, policy distillation, imitation and reinforcement learning) to improve model interpretability and action quality.
-
Collaborate with infrastructure engineers to scale training across thousands of GPUs using distributed training frameworks (FSDP, DDP, etc.).
-
Conduct systematic ablation, evaluation, and visualization of model behavior across perception, reasoning, and planning tasks.
-
Contribute to model deployment optimization, including quantization, export, and latency–accuracy trade-offs for onboard execution.
Minimum Qualifications
-
Master’s degree or higher in Computer Science, Electrical/Computer Engineering, or related field, with 5 - 8 + years of experience in deep learning research or productization.
-
Strong proficiency in PyTorch and modern transformer-based model design.
-
Experience in large-scale pretraining or multi-modal modeling (vision, language, or planning).
-
Deep understanding of representation learning, temporal modeling, and self-supervised or reinforcement learning techniques.
-
Familiarity with distributed training (DDP, FSDP) and large-batch optimization.
Preferred Qualifications
-
PhD in CS/CE/EE or related field, with 1+ years of relevant industry experience.
-
Publication record in top-tier AI conferences (CVPR, ICCV, NeurIPS, ICLR, ICML, ECCV).
-
Prior experience building foundation or end-to-end driving models, or LLM/VLM architectures (e.g., ViT, Flamingo, BEVFormer, RT-2, or GRPO-style policies).
-
Familiarity with RLHF/DPO/GRPO, trajectory prediction, or policy learning for control tasks.
-
Proven ability to collaborate cross-functionally with infra, perception, and planning teams to deliver production-ready models.
What do we provide:
-
A collaborative, research-driven environment with access to massive real-world data and industry-scale compute.
-
An opportunity to work with top-tier researchers and engineers advancing the frontier of foundation models for autonomous driving.
- Direct impact on the next generation of intelligent mobility systems.
-
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.
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.
Ready to apply?
Apply to XPENG
XPENG
View all jobs →
Senior Staff Machine Learning Engineer – End-to-End Autonomous Driving
Share this job
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 Deep Learning Engineers with strong expertise in machine learning (ML) and deep learning (DL) system design, along with solid software development skills. In this role, you will research, implement, and evaluate a unified end-to-end onboard model leveraging state-of-the-art technologies, including transformer-based architectures, diffusion models, reinforcement learning, and Vision-Language-Action (VLA) models. You will collaborate with a world-class team of experts in computer vision, AI systems, and software engineering to push the boundaries of autonomous vehicle performance. Your work will be powered by vast amounts of real-world multimodal data from our autonomous fleet, enabling the development of next-generation AI-driven driving solutions.
Job Responsibilities:
-
Research and develop cutting-edge deep learning algorithms for a unified, end-to-end onboard model that seamlessly integrates perception, prediction, and planning, replacing traditional modular model pipelines.
-
Research and develop Vision-Language-Action (VLA) models to enable context-aware, multimodal decision-making, allowing the model to understand visual, textual, and action-based cues for enhanced driving intelligence.
-
Design and optimize highly efficient neural network architectures, ensuring they achieve low-latency, real-time execution on the vehicle’s high-performance computing platform, balancing accuracy, efficiency, and robustness.
-
Develop and scale an offline machine learning (ML) infrastructure to support rapid adaptation, large-scale training, and continuous self-improvement of end-to-end models, leveraging self-supervised learning, imitation learning, and reinforcement learning.
-
Deliver production-quality onboard software, working closely with sensor fusion, mapping, and perception teams to build the industry’s most intelligent and adaptive autonomous driving system.
-
Leverage massive real-world datasets collected from our autonomous fleet, integrating multi-modal sensor data to train and refine state-of-the-art end-to-end driving models.
-
Design, conduct, and analyze large-scale experiments, including sim-to-real transfer, closed-loop evaluation, and real-world testing to rigorously benchmark model performance and generalization.
-
Collaborate with system software engineers to deploy high-performance deep learning models on embedded automotive hardware, ensuring real-world robustness and reliability under diverse driving conditions.
-
Work cross-functionally with AI researchers, computer vision experts, and autonomous driving engineers to push the frontier of end-to-end learning, leveraging advances in transformer-based architectures, diffusion models, and reinforcement learning to redefine the future of autonomous mobility.
Minimum Skill Requirements:
-
MS or PhD level education in Engineering or Computer Science with a focus on Deep Learning, Artificial Intelligence, or a related field, or equivalent experience.
-
Strong experience in applied deep learning including model architecture design, model training, data mining, and data analytics.
-
5 - 8 years + of experience working with DL frameworks such as PyTorch, Tensorflow.
-
Strong Python programming experience with software design skills.
-
Solid understanding of data structures, algorithms, code optimization and large-scale data processing.
-
Excellent problem-solving skills.
Preferred Skill Requirements:
-
Hands on experience in developing DL based planning engine for autonomous driving.
-
Experience in applying CNN/RNN/GNN, attention model, or time series analysis to real world problems.
-
Experience in other ML/DL applications, e.g., reinforcement learning.
-
Experience in DL model deployment and optimization tools such as ONNX and TensorRT.
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.
Ready to apply?
Apply to XPENG
XPENG
View all jobs →
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 full-time Machine Learning Engineer / Research Scientist to drive the modeling and algorithmic development of XPENG’s next-generation Vision-Language-Action (VLA) Foundation Model — the core brain that powers our end-to-end autonomous driving systems.
You will work closely with world-class researchers, perception and planning engineers, and infrastructure experts to design, train, and deploy large-scale multi-modal models that unify vision, language, and control. Your work will directly shape the intelligence that enables XPENG’s future L3/L4 autonomous driving products.
Key Responsibilities
-
Design and implement large-scale multi-modal architectures (e.g., vision–language–action transformers) for end-to-end autonomous driving.
-
Develop pretraining and fine-tuning strategies leveraging massive labeled and unlabeled fleet data (images, video, LiDAR, CAN bus, maps, human driving behaviors, etc.).
-
Research and integrate cross-modal alignment (e.g., visual grounding, temporal reasoning, policy distillation, imitation and reinforcement learning) to improve model interpretability and action quality.
-
Collaborate with infrastructure engineers to scale training across thousands of GPUs using distributed training frameworks (FSDP, DDP, etc.).
-
Conduct systematic ablation, evaluation, and visualization of model behavior across perception, reasoning, and planning tasks.
-
Contribute to model deployment optimization, including quantization, export, and latency–accuracy trade-offs for onboard execution.
Minimum Qualifications
-
Master’s degree or higher in Computer Science, Electrical/Computer Engineering, or related field, with 3+ years of experience in deep learning research or productization.
-
Strong proficiency in PyTorch and modern transformer-based model design.
-
Experience in large-scale pretraining or multi-modal modeling (vision, language, or planning).
-
Deep understanding of representation learning, temporal modeling, and self-supervised or reinforcement learning techniques.
-
Familiarity with distributed training (DDP, FSDP) and large-batch optimization.
Preferred Qualifications
-
PhD in CS/CE/EE or related field, with 1+ years of relevant industry experience.
-
Publication record in top-tier AI conferences (CVPR, ICCV, NeurIPS, ICLR, ICML, ECCV).
-
Prior experience building foundation or end-to-end driving models, or LLM/VLM architectures (e.g., ViT, Flamingo, BEVFormer, RT-2, or GRPO-style policies).
-
Familiarity with RLHF/DPO/GRPO, trajectory prediction, or policy learning for control tasks.
-
Proven ability to collaborate cross-functionally with infra, perception, and planning teams to deliver production-ready models.
What do we provide:
-
A collaborative, research-driven environment with access to massive real-world data and industry-scale compute.
-
An opportunity to work with top-tier researchers and engineers advancing the frontier of foundation models for autonomous driving.
- Direct impact on the next generation of intelligent mobility systems.
-
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.
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?
Apply to XPENG
XPENG
View all jobs →
Senior Machine Learning Engineer – Perception/ End-to-End
Share this job
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 Deep Learning Engineers with strong expertise in machine learning (ML) and deep learning (DL) system design, along with solid software development skills. In this role, you will research, implement, and evaluate a unified end-to-end onboard model leveraging state-of-the-art technologies, including transformer-based architectures, diffusion models, reinforcement learning, and Vision-Language-Action (VLA) models. You will collaborate with a world-class team of experts in computer vision, AI systems, and software engineering to push the boundaries of autonomous vehicle performance. Your work will be powered by vast amounts of real-world multimodal data from our autonomous fleet, enabling the development of next-generation AI-driven driving solutions.
Job Responsibilities:
-
Research and develop cutting-edge deep learning algorithms for a unified, end-to-end onboard model that seamlessly integrates perception, prediction, and planning, replacing traditional modular model pipelines.
-
Research and develop Vision-Language-Action (VLA) models to enable context-aware, multimodal decision-making, allowing the model to understand visual, textual, and action-based cues for enhanced driving intelligence.
-
Address real-world challenges by enhancing online mapping, occupancy grid, and 3D detection models. Have deep expertise in perception systems and demonstrate strong problem-solving skills in analyzing and resolving production-level corner cases.
-
Design and optimize highly efficient neural network architectures, ensuring they achieve low-latency, real-time execution on the vehicle’s high-performance computing platform, balancing accuracy, efficiency, and robustness.
-
Develop and scale an offline machine learning (ML) infrastructure to support rapid adaptation, large-scale training, and continuous self-improvement of end-to-end models, leveraging self-supervised learning, imitation learning, and reinforcement learning.
-
Deliver production-quality onboard software, working closely with sensor fusion, mapping, and perception teams to build the industry’s most intelligent and adaptive autonomous driving system.
-
Leverage massive real-world datasets collected from our autonomous fleet, integrating multi-modal sensor data to train and refine state-of-the-art end-to-end driving models.
-
Design, conduct, and analyze large-scale experiments, including sim-to-real transfer, closed-loop evaluation, and real-world testing to rigorously benchmark model performance and generalization.
-
Collaborate with system software engineers to deploy high-performance deep learning models on embedded automotive hardware, ensuring real-world robustness and reliability under diverse driving conditions.
-
Work cross-functionally with AI researchers, computer vision experts, and autonomous driving engineers to push the frontier of end-to-end learning, leveraging advances in transformer-based architectures, diffusion models, and reinforcement learning to redefine the future of autonomous mobility.
Minimum Skill Requirements:
-
MS or PhD level education in Engineering or Computer Science with a focus on Deep Learning, Artificial Intelligence, or a related field, or equivalent experience.
-
Strong experience in applied deep learning including model architecture design, model training, data mining, and data analytics.
-
1-3 years + of experience working with DL frameworks such as PyTorch, Tensorflow.
-
Strong Python programming experience with software design skills.
-
Solid understanding of data structures, algorithms, code optimization and large-scale data processing.
-
Excellent problem-solving skills.
Preferred Skill Requirements:
-
Hands on experience in developing DL based planning engine for autonomous driving.
-
Experience in applying CNN/RNN/GNN, attention model, or time series analysis to real world problems.
-
Experience in other ML/DL applications, e.g., reinforcement learning.
-
Experience in DL model deployment and optimization tools such as ONNX and TensorRT.
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?
Apply to XPENG
XPENG
View all jobs →
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.
About the Role
We are actively seeking a full-time Research Scientist Intern to drive the modeling and algorithmic development of XPENG’s next-generation Vision-Language-Action (VLA) Foundation Model — the core brain that powers our end-to-end autonomous driving systems.
You will work closely with world-class researchers, perception and planning engineers, and infrastructure experts to design, train, and deploy large-scale multi-modal models that unify vision, language, and control. Your work will directly shape the intelligence that enables XPENG’s future L3/L4 autonomous driving products.
Key Responsibilities
-
Conduct research on designing and implementing large-scale multi-modal architectures (e.g., vision–language–action transformers) for end-to-end autonomous driving.
-
Design and integrate cross-modal alignment (e.g., visual grounding, temporal reasoning, policy distillation, imitation and reinforcement learning) to improve model interpretability and action quality.
-
Closely collaborate with researchers and engineers across the modeling and infrastructure team.
-
Contribute to top-tier AI/CV/ML conferences publications and present research findings.
Minimum Qualifications
-
Currently enrolled in the Master/Ph.D program in Computer Science, Electrical/Computer Engineering, or related field, with the specialization in the CV/NLP/ML.
-
Experience in multi-modal modeling (vision, language, or planning), with deep understanding of representation learning, temporal modeling, and reinforcement learning techniques.
-
Strong proficiency in PyTorch and modern transformer-based model design.
Preferred Qualifications
-
Publication record in top-tier AI conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, etc).
-
Prior experience building foundation or end-to-end driving models, or LLM/VLM architectures (e.g., ViT, Flamingo, BEVFormer, RT-2, or GRPO-style policies).
-
Knowledge of RLHF/DPO/GRPO, trajectory prediction, or policy learning for control tasks.
-
Familiarity with distributed training (DDP, FSDP) and large-batch optimization.
What We Offer
-
A collaborative, research-driven environment with access to massive real-world data and industry-scale compute.
-
Opportunity to work with top-tier researchers and engineers advancing the frontier of foundation models for autonomous driving.
-
Direct impact on the next generation of intelligent mobility systems.
-
Comprehensive benefits, meals, and team-building activities.
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.
Ready to apply?
Apply to XPENG
XPENG
View all jobs →
Senior Machine Learning Engineer - Foundation Model
Share this job
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 full-time Machine Learning Engineer / Research Scientist to drive the modeling and algorithmic development of XPENG’s next-generation Vision-Language-Action (VLA) Foundation Model — the core brain that powers our end-to-end autonomous driving systems.
You will work closely with world-class researchers, perception and planning engineers, and infrastructure experts to design, train, and deploy large-scale multi-modal models that unify vision, language, and control. Your work will directly shape the intelligence that enables XPENG’s future L3/L4 autonomous driving products.
Key Responsibilities
-
Design and implement large-scale multi-modal architectures (e.g., vision–language–action transformers) for end-to-end autonomous driving.
-
Develop pretraining and fine-tuning strategies leveraging massive labeled and unlabeled fleet data (images, video, LiDAR, CAN bus, maps, human driving behaviors, etc.).
-
Research and integrate cross-modal alignment (e.g., visual grounding, temporal reasoning, policy distillation, imitation and reinforcement learning) to improve model interpretability and action quality.
-
Collaborate with infrastructure engineers to scale training across thousands of GPUs using distributed training frameworks (FSDP, DDP, etc.).
-
Conduct systematic ablation, evaluation, and visualization of model behavior across perception, reasoning, and planning tasks.
-
Contribute to model deployment optimization, including quantization, export, and latency–accuracy trade-offs for onboard execution.
Minimum Qualifications
-
Master’s degree or higher in Computer Science, Electrical/Computer Engineering, or related field, with 3+ years of experience in deep learning research or productization.
-
Strong proficiency in PyTorch and modern transformer-based model design.
-
Experience in large-scale pretraining or multi-modal modeling (vision, language, or planning).
-
Deep understanding of representation learning, temporal modeling, and self-supervised or reinforcement learning techniques.
-
Familiarity with distributed training (DDP, FSDP) and large-batch optimization.
Preferred Qualifications
-
PhD in CS/CE/EE or related field, with 1+ years of relevant industry experience.
-
Publication record in top-tier AI conferences (CVPR, ICCV, NeurIPS, ICLR, ICML, ECCV).
-
Prior experience building foundation or end-to-end driving models, or LLM/VLM architectures (e.g., ViT, Flamingo, BEVFormer, RT-2, or GRPO-style policies).
-
Familiarity with RLHF/DPO/GRPO, trajectory prediction, or policy learning for control tasks.
-
Proven ability to collaborate cross-functionally with infra, perception, and planning teams to deliver production-ready models.
What do we provide:
-
A collaborative, research-driven environment with access to massive real-world data and industry-scale compute.
-
An opportunity to work with top-tier researchers and engineers advancing the frontier of foundation models for autonomous driving.
- Direct impact on the next generation of intelligent mobility systems.
-
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.
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?
Apply to XPENG
XPENG
View all jobs →