
Pioneering accelerated computing and AI
Parking Perception DNN Engineer
必須スキル
PyTorch
Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. Now, NVIDIA’s GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world.
We are now looking for an extraordinary Senior Perception Engineer to develop and productize NVIDIA’s autonomous driving solutions. As a member of our perception team, you will work on building world-class 3D obstacle perception solutions based on multi-sensor fusion, including cameras, ultrasonic sensors, and radar, to estimate high-resolution reconstruction of the world, such as occupancy networks. The primary approach will be deep learning. You will be challenged to improve robustness and accuracy as well as efficiency of the solutions to fully enable autonomous driving anywhere and anytime.
What you’ll be doing:
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Perception experts with application focus will be on multi-sensor fusion based deep learning model development for obstacle perception/fusion in complex driving environments.
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Applied research and development of innovative deep learning and multi-sensor fusion algorithms to improve output accuracy of 3D obstacle perception solutions under challenging and diverse scenarios, with a focus on high-resolution world reconstruction (e.g., occupancy networks).
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Identify and analyze the strength and weakness of the developed 3D obstacle perception solutions using large scale benchmark data (both real and synthetic) and improve them iteratively through KPI building and optimization. This includes careful data verification, model architecture design, understanding details of loss function engineering, and being capable of finding detailed ML bugs and iterating toward perfection.
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Productize the developed 3D obstacle perception solutions by meeting product requirements for safety, latency, and SW robustness, with a strong emphasis on production deep learning model development.
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Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy. Efforts will include data collection prioritization and planning, labeling prioritization, so that value of data is maximized.
What we need to see:
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10+ years of hands-on work experience in developing deep learning and algorithms to solve sophisticated real world problems, and proficiency in using deep learning frameworks (e.g., Py Torch).
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Experience in multi-sensor fusion (cameras, ultrasonic sensors, radar) for perception tasks, particularly in high-resolution world reconstruction.
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Proven experience in production deep learning model development, including careful data verification, model architecture design, loss function engineering, and debugging ML models.
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Experience in data-driven development and collaboration with data and ground truth teams.
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Strong programming skills in python and/or C++.
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Outstanding communication and teamwork skills as we work as a tightly-knit team, always discussing and learning from each other.
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BS/MS/PhD in CS, EE, sciences or related fields (or equivalent experience)
Ways to stand out from the crowd:
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Experience on end-to-end deep learning model development is a plus.
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Proven expertise in developing perception solutions for autonomous driving or robotics using deep learning with multi-sensor input.
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Hands-on experience in developing and deploying DNN-based solutions to embedded platforms for real time applications.
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Good understanding of fundamentals of 3D computer vision, camera calibrations including intrinsic and extrinsic, and sensor fusion principles.
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Experience with development in CUDA language. The ability to implement CUDA kernels as part of training or inference pipelines.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until March 14, 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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NVIDIAについて

NVIDIA
PublicA computing platform company operating at the intersection of graphics, HPC, and AI.
10,001+
従業員数
Santa Clara
本社所在地
$4.57T
企業価値
レビュー
10件のレビュー
4.4
10件のレビュー
ワークライフバランス
2.8
報酬
4.5
企業文化
4.2
キャリア
4.3
経営陣
3.8
78%
知人への推奨率
良い点
Cutting-edge technology and innovation
Excellent compensation and benefits
Great team culture and collaboration
改善点
High pressure and expectations
Poor work-life balance and long hours
Fast-paced environment leading to burnout
給与レンジ
79件のデータ
L3
L4
L5
L3 · Data Scientist IC2
0件のレポート
$177,542
年収総額
基本給
-
ストック
-
ボーナス
-
$150,910
$204,174
面接レビュー
レビュー5件
難易度
3.0
/ 5
面接プロセス
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
よくある質問
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Past Experience
最新情報
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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NVIDIA Culture Discussions
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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