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求人NVIDIA

Senior Software Engineer, AI and DL Kernel Libraries

NVIDIA

Senior Software Engineer, AI and DL Kernel Libraries

NVIDIA

US

·

On-site

·

Full-time

·

3w ago

必須スキル

Python

PyTorch

TensorFlow

Machine Learning

We're looking for outstanding AI systems engineers to develop groundbreaking technologies in the inference systems software stack! We build innovative AI systems software to accelerate for AI inference. As a member of the team, you'll develop libraries, code generators, and GPU kernel technologies for NVIDIA's hardware architecture. This means designing and building things like new abstractions, efficient attention kernel implementations, new LLM inference runtimes components, and kernel code generators to accelerate large language models, agents, and other high-impact AI workloads.

What you'll be doing:

  • Innovating and developing new AI systems technologies for efficient inference

  • Designing, implementing, and optimizing kernels for high impact AI workloads

  • Designing and implementing extensible abstractions for LLM serving engines

  • Building efficient just-in-time domain specific compilers and runtimes

  • Collaborating closely with other engineers at NVIDIA across deep learning frameworks, libraries, kernels, and GPU arch teams

  • Contributing to open source communities like Flash Infer, vLLM, and SGLang

What we need to see:

  • Masters degree in Computer Science, Electrical Engineering, or related field (or equivalent experience); PhD are preferred

  • 6+ years (academic/ industry) experience with ML/DL systems development preferable

  • Strong experience in developing or using deep learning frameworks (e.g. Py Torch, JAX, Tensor Flow, ONNX, etc) and ideally inference engines and runtimes such as vLLM, SGLang, and MLC.

  • Strong Python and C/C++ programming skills

  • Strong experience in GPU kernel development and performance optimizations (especially using CUDA C/C++, cu Tile, Triton, or similar)

Ways to stand out from the crowd:

  • Background in domain specific compiler and library solutions for LLM inference and training (e.g. Flash Infer, Flash Attention)

  • Expertise in inference engines like v

LLM and SGLang:

  • Expertise in machine learning compilers (e.g. Apache TVM, MLIR)

  • Open source project ownership or contributions

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.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until March 15, 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

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

47件のデータ

Junior/L3

Mid/L4

Junior/L3 · Analyst

7件のレポート

$170,275

年収総額

基本給

$130,981

ストック

-

ボーナス

-

$155,480

$234,166

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