Senior Staff Machine Learning Engineer – Autonomous Driving Foundation Models
Santa Clara, CA
- Pay
$244,140–413,160/year · BaseAnnual period assumed — pay source
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.
Read the full posting- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingLead design of end-to-end VLA architectures linking perception, language reasoning and actions
Build generative world models for closed-loop driving simulation and evaluation
Refine driving policies with online/offline RL and imitation learning
Define scaling laws and oversee data curation, auto-labeling and post-training
Lead adaptation of the model for overseas road conditions and traffic laws
From the employer’s posting
Lead the design of end-to-end VLA architectures
Drive R&D in generative world models (latent dynamics) to create high-fidelity, controllable driving simulations
Apply Advanced RL (Online/Offline) and IL to refine driving policies
Define scaling laws for driving foundation models, overseeing data curation, automated labeling, and post-training
Lead the model’s adaptation strategy for overseas road conditions
What you’ll bring
All qualificationsCore experience
- 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.
Qualification wording
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.
Tools in this posting
- PyTorch
Source — Tool mentions in context
- 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.
Posting record
Report an errorOpen 6 monthsWe first saw this posting 199 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Apr 14, 2026 · 199 days ago
- Recorded sightings
- 133
- Last seen by us
- Oct 31, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
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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: 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:
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Architectural Leadership: Lead the design of end-to-end VLA architectures, bridging multi-modal perception with high-level linguistic reasoning and precise action generation.
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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.
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Policy Evolution: Apply Advanced RL (Online/Offline) and IL to refine driving policies, focusing on long-horizon planning and complex multi-agent interactions.
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Scaling & Data Strategy: Define scaling laws for driving foundation models, overseeing data curation, automated labeling, and post-training at a multi-billion parameter scale.
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Global Generalization: Lead the model’s adaptation strategy for overseas road conditions, ensuring robust performance across varying traffic laws and driving cultures.
Qualifications:
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5-8 years of expertise in Deep Learning, with a significant track record in VLM, VLA, or Embodied AI.
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Proven experience in training and deploying Foundation Models (Transformers, LLMs) at scale.
-
Deep understanding of Sequential Decision Making, World Models, or Policy Gradient methods.
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Mastery of PyTorch and expertise in distributed training (DeepSpeed, Megatron, etc.).
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A "Product-First" mindset: The ability to balance cutting-edge research with the deterministic requirements of L4 production vehicles.
What do we provide:
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A fun, supportive and engaging environment
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Infrastructures and computational resources to support your ML model development/research.
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Opportunity to work on cutting edge technologies with the top talent in the field.
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Opportunity to make significant impact on 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 $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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
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.
- 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.
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