招聘
必备技能
Machine Learning
We are looking for a passionate team member to contribute to our autonomous driving product! The role involves applying deep learning and computer vision technologies to 3D/4D world modeling for autonomous vehicles. Our work is crucial to empower the training and testing of end-to-end driving models through end-to-end close-loop simulation. You will have the opportunity to work with a diverse team of researchers and engineers in the field of 3D/4D reconstruction, world modeling and simulation to deliver impact to our customers around the world!
What you’ll be doing:
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Build innovative world reconstruction systems using large geometry models, perception models, Gaussian splatting, diffusion models, and generative world models.
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Invent evaluation methods to measure the reconstruction and simulation quality.
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Develop tools to visualize and triage evaluation metrics.
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Build automated and agentic workflows to boost developer efficiency.
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Profile and optimize the performance of neural networks training and deployment.
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Relentlessly improve the reconstruction fidelity and simulation realism.
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Collaborate with scientists and developers across several organizations.
What we need to see:
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BS, MS, or PhD degree or equivalent experience in Engineering or Computer Science with a focus on Generative AI, Deep Learning, Computer Vision, Robotics, Computer Graphics, or a related field.
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5+ years Hands on experience with structure from motion, dense reconstruction, Gaussian splatting, diffusion models, and/or generative world models.
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Solid fundamentals in 3D computer vision and deep learning.
Ways to stand out from the crowd:
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Experience in autonomous driving: evidence of practical experiments and projects within the autonomous driving domain, showcasing your ability to apply machine learning algorithms to solve sophisticated problems in this field.
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Published research: particularly around Gaussian splatting, image/video diffusion models, or generative world models demonstrating a deep understanding and contribution to the field.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
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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
Junior/L3
Mid/L4
Junior/L3 · Analyst
7份报告
$170,275
年薪总额
基本工资
$130,981
股票
-
奖金
-
$155,480
$234,166
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
新闻动态
Negotiating NVIDIA's Offer
Base, stock, and sign-on negotiable. Recruiters invested in closing candidates. CEO reviews all 42K employee salaries monthly. Stock growth has made many employees millionaires.
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NVIDIA Company Reviews
WLB rated 3.9/5 (lowest category). 64% satisfied with WLB but 53% feel burnt out. Compensation rated 4.4-4.5/5. Experience highly team-dependent.
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NVIDIA Interview Discussions
Technical bar is high with 4-6 rounds. Process takes 4-8 weeks. Expect C++ questions, LeetCode medium, and system design. Difficulty rated 3.16/5.
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Team-dependent experience; sink-or-swim culture that rewards high performers but can be overwhelming. No politics, flat structure, but demanding workload with some teams requiring evening/weekend work.
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