Staff Machine Learning Engineer - Foundation Model
Santa Clara, CA
- Pay
$215,280–364,320/year · BaseAnnual period assumed — pay source
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.
Read the full posting- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingDesign 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.).
Collaborate with infrastructure engineers to scale training across thousands of GPUs using distributed training frameworks (FSDP, DDP, etc.).
From the employer’s posting
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.).
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.
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.
What you’ll bring
All qualificationsCore experience
- 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 experience
- 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.
Qualification wording
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.
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.
Tools in this posting
- PyTorch
Source — Tool mentions in context
- 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).
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.
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
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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.
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Contribute to model deployment optimization, including quantization, export, and latency–accuracy trade-offs for onboard execution.
Minimum Qualifications
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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
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PhD in CS/CE/EE or related field, with 1+ years of relevant industry experience.
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Publication record in top-tier AI conferences (CVPR, ICCV, NeurIPS, ICLR, ICML, ECCV).
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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).
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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:
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A collaborative, research-driven environment with access to massive real-world data and industry-scale compute.
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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.
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Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving.
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Competitive compensation package.
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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.
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
Already applied? Track this application
Source & posting history
Source notes
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- Pay
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.
- Location & working pattern
Santa Clara, CA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
- Status in our records
- Active
- First seen by us
- Apr 14, 2026
- Recorded sightings
- 114
- Last seen by us
- Oct 8, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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