招聘
必备技能
Python
Machine Learning
At NVIDIA, we are seeking exceptional engineers to join our autonomous driving team to design, implement, and deploy cutting-edge end-to-end autonomous driving systems, running on NVIDIA chips in mass-production vehicles. Our strategy has evolved from AI 1.0 — building a driver from scratch — to AI 2.0 — teaching an intelligent agent to drive. This next phase leverages LLMs, VLMs, and VLAs to bring unprecedented reasoning, planning capabilities, and interactivity with the driving system to autonomous vehicles and general robotics. Let’s build the future of autonomy—together!
What You’ll Be Doing:
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Design and train innovative large-scale models—including generative, imitation, and reinforcement learning—to improve the planning and reasoning capabilities of our driving systems.
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Build, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics applications.
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Explore novel data generation and collection strategies to improve diversity and quality of training datasets.
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Collaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability standards are met.
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Integrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software.
What We Need to See:
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Hands-on experience building LLMs, VLMs, or VLAs from scratch or a proven track record as a top-tier coder passionate about autonomous systems.
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Deep understanding of modern deep learning architectures and optimization techniques.
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Proven record of deploying production-grade ML models for self-driving, robotics, or related fields at scale.
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Strong programming skills in Python and proficiency with major deep learning frameworks.
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Familiarity with C++ for model deployment and integration in safety-critical systems.
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Master's degree (or equivalent experience) with 13+ years of work experience in AV or related field or PhD with 11 years of work experience in AV or related field.
Ways to Stand Out from the Crowd:
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Experience with LLM/VLM/VLA systems deployable to autonomous vehicles or general robotics.
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Publications, open-source contributions, or competition wins related to LLM/VLM/VLA systems.
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Deep understanding of behavior and motion planning in real-world AV applications.
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Experience building and training large-scale datasets and models.
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Proven ability to optimize algorithms for real-time performance in resource-constrained environments and strong track record of taking projects from concept to production deployment.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until March 23, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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关于NVIDIA

NVIDIA
PublicA computing platform company operating at the intersection of graphics, HPC, and AI.
10,001+
员工数
Santa Clara
总部位置
$4.57T
企业估值
评价
4.1
10条评价
工作生活平衡
3.5
薪酬
4.2
企业文化
4.3
职业发展
4.5
管理层
4.0
75%
推荐给朋友
优点
Great culture and supportive environment
Smart colleagues and excellent people
Cutting-edge technology and learning opportunities
缺点
Team-dependent experience and outcomes
Work-life balance issues with long hours
Politics and influence over competence
薪资范围
73个数据点
L3
L4
L5
L3 · Data Scientist IC2
0份报告
$177,542
年薪总额
基本工资
-
股票
-
奖金
-
$150,910
$204,174
面试经验
7次面试
难度
3.1
/ 5
体验
正面 0%
中性 86%
负面 14%
面试流程
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Interview
5
System Design Interview
6
Team Review
常见问题
Coding/Algorithm
System Design
Technical Knowledge
Behavioral/STAR
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