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求人NVIDIA

Deep Learning Engineer - LLM and VLM Model Compression

NVIDIA

Deep Learning Engineer - LLM and VLM Model Compression

NVIDIA

Poland; Switzerland

·

On-site

·

Full-time

·

1mo ago

必須スキル

Python

PyTorch

TensorFlow

We are looking for DL engineers passionate about building deep learning frameworks for large language (LLM) and vision language (VLM) model compression that push the boundaries of AI efficiency. In this role, you’ll collaborate with world-class teams across NVIDIA to advance both the software and hardware stack that powers modern AI.

Join the team building software used by the entire world. Work with world class engineers and researchers to build next-generation deep learning frameworks for compressing LLM and VLM models through pruning, distillation, and neural architecture search (NAS). Work on most powerful, enterprise-grade GPU clusters capable of hundreds of Peta FLOPS and on unreleased hardware before anyone in the world. Are you ready for this challenge?

What you’ll be doing:

  • Design and implement a deep learning framework for compressing large language and vision-language models to deliver highly optimized, high-performance AI systems used worldwide.

  • Develop and integrate new algorithms for pruning, NAS, and distillation in collaboration with NVIDIA researchers and engineers.

  • Experiment with compressing the latest LLMs and VLMs, analyzing their performance and behavior across diverse workloads.

  • Collaborate with researchers and engineers across NVIDIA, providing guidance on improving the design, usability and performance of workloads.

  • Lead best-practices for building, testing, and releasing DL software.

What we need to see:

  • 8+ years of experience in Deep Learning and SW Development.

  • BSc, MS or PhD degree in Computer Science, Computer Architecture or related technical field.

  • Hands-on experience with LLM or VLM model training or inference.

  • Excellent Python programming skills.

  • Extensive knowledge of at least one DL Framework (Py Torch, Tensor Flow, JAX, Mx Net) with practical experience in Py Torch required.

  • Strong problem solving and analytical skills.

  • Algorithms and DL fundamentals.

Ways to stand out from the crowd:

  • Experience applying and implementing model compression techniques such as pruning, NAS, distillation, and quantization.

  • Experience building deep learning frameworks for training, inference, model compression, or related topic.

  • GPU programming experience (CUDA or OpenCL) is a plus but not required.

  • First-author publication in a top-tier deep learning or AI conference.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most brilliant and forward-thinking people in the world working for us. If you're creative and autonomous, we want to hear from you! 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, gender, sexual orientation, age, marital status, veteran status, or disability status.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. For Poland: The base salary range is 292,500 PLN - 507,000 PLN for Level 4, and 375,000 PLN - 650,000 PLN for Level 5.

#deeplearning

総閲覧数

1

応募クリック数

0

模擬応募者数

0

スクラップ

0

NVIDIAについて

NVIDIA

NVIDIA

Public

A 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

4.1

10件のレビュー

ワークライフバランス

3.5

報酬

4.2

企業文化

4.3

キャリア

4.5

経営陣

4.0

75%

友人に勧める

良い点

Great culture and supportive environment

Smart colleagues and excellent people

Cutting-edge technology and learning opportunities

改善点

Team-dependent experience and outcomes

Work-life balance issues with long hours

Politics and influence over competence

給与レンジ

73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

面接体験

7件の面接

難易度

3.1

/ 5

体験

ポジティブ 0%

普通 86%

ネガティブ 14%

面接プロセス

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Interview

5

System Design Interview

6

Team Review

よくある質問

Coding/Algorithm

System Design

Technical Knowledge

Behavioral/STAR