XPENG & Volkswagen Group
Senior Staff Machine Learning Engineer - Foundation Model
XPENG & Volkswagen Group, Santa Clara, California, us, 95053
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.
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.
Voluntary Self-Identification For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.
As set forth in XPENG’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
Voluntary Self-Identification of Disability Form CC‑305
Page 1 of 1
OMB Control Number 1250‑0005
Expires 04/30/2026
Why are you being asked to complete this form?
We are a federal contractor or subcontractor. The law requires us to provide equal employment opportunity to qualified people with disabilities. We have a goal of having at least 7% of our workers as people with disabilities. The law says we must measure our progress towards this goal. To do this, we must ask applicants and employees if they have a disability or have ever had one. People can become disabled, so we need to ask this question at least every five years.
Completing this form is voluntary, and we hope that you will choose to do so. Your answer is confidential. No one who makes hiring decisions will see it. Your decision to complete the form and your answer will not harm you in any way. If you want to learn more about the law or this form, visit the U.S. Department of Labor’s Office of Federal Contract Compliance Programs (OFCCP) website at www.dol.gov/ofccp.
How do you know if you have a disability?
A disability is a condition that substantially limits one or more of your “major life activities.” If you have or have ever had such a condition, you are a person with a disability.
Disabilities include, but are not limited to:
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Missing limbs or partially missing limbs
Mobility impairment, benefiting from the use of a wheelchair, scooter, walker, leg brace(s) and/or other supports
Nervous system condition, for example, migraine headaches, Parkinson’s disease, multiple sclerosis (MS)
Neurodivergence, for example, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, dyslexia, dyspraxia, other learning disabilities
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Traumatic brain injury
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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.
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.
Voluntary Self-Identification For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.
As set forth in XPENG’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
Voluntary Self-Identification of Disability Form CC‑305
Page 1 of 1
OMB Control Number 1250‑0005
Expires 04/30/2026
Why are you being asked to complete this form?
We are a federal contractor or subcontractor. The law requires us to provide equal employment opportunity to qualified people with disabilities. We have a goal of having at least 7% of our workers as people with disabilities. The law says we must measure our progress towards this goal. To do this, we must ask applicants and employees if they have a disability or have ever had one. People can become disabled, so we need to ask this question at least every five years.
Completing this form is voluntary, and we hope that you will choose to do so. Your answer is confidential. No one who makes hiring decisions will see it. Your decision to complete the form and your answer will not harm you in any way. If you want to learn more about the law or this form, visit the U.S. Department of Labor’s Office of Federal Contract Compliance Programs (OFCCP) website at www.dol.gov/ofccp.
How do you know if you have a disability?
A disability is a condition that substantially limits one or more of your “major life activities.” If you have or have ever had such a condition, you are a person with a disability.
Disabilities include, but are not limited to:
Alcohol or other substance use disorder (not currently using drugs illegally)
Autoimmune disorder, for example, lupus, fibromyalgia, rheumatoid arthritis, HIV/AIDS
Blind or low vision
Cancer (past or present)
Cardiovascular or heart disease
Celiac disease
Cerebral palsy
Deaf or serious difficulty hearing
Diabetes
Disfigurement, for example, disfigurement caused by burns, wounds, accidents, or congenital disorders
Epilepsy or other seizure disorder
Gastrointestinal disorders, for example, Crohn’s Disease, irritable bowel syndrome
Intellectual or developmental disability
Mental health conditions, for example, depression, bipolar disorder, anxiety disorder, schizophrenia, PTSD
Missing limbs or partially missing limbs
Mobility impairment, benefiting from the use of a wheelchair, scooter, walker, leg brace(s) and/or other supports
Nervous system condition, for example, migraine headaches, Parkinson’s disease, multiple sclerosis (MS)
Neurodivergence, for example, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, dyslexia, dyspraxia, other learning disabilities
Partial or complete paralysis (any cause)
Pulmonary or respiratory conditions, for example, tuberculosis, asthma, emphysema
Short stature (dwarfism)
Traumatic brain injury
Disability Status Select...
PUBLIC BURDEN STATEMENT:
According to the Paperwork Reduction Act of 1995 no persons are required to respond to a collection of information unless such collection displays a valid OMB control number. This survey should take about 5 minutes to complete.
#J-18808-Ljbffr