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求人NVIDIA

Senior Deep Learning Framework Communications Engineer

NVIDIA

Senior Deep Learning Framework Communications Engineer

NVIDIA

US, CA, Santa Clara

·

On-site

·

Full-time

·

2mo ago

報酬

$152,000 - $287,500

福利厚生

Parental Leave

Equity

Learning

Healthcare

必須スキル

Python

JavaScript

TypeScript

About the Role

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. We are looking for a motivated Deep Learning engineer to bring advanced communication technologies into AI stacks, including Py Torch, TRT-LLM, vLLM, SGLang, JAX, etc. You will be working with the team that created communication libraries like NCCL, NVSHMEM & technology like GPUDirect -- for scaling Deep Learning and HPC applications. Your customers will have diverse multi-GPU demands, ranging from training on scales up to 100K GPUs to inference down at microsecond latency. Communication performance between the GPUs has a direct impact on AI applications. Your work in AI toolkits will make all of those easier for the community. This is an outstanding opportunity for someone with an AI background to advance the state of the art in this space. Are you ready to contribute to the development of innovative technologies and help realize NVIDIA's vision?

Responsibilities

  • Integrate new communication libraries features in AI frameworks: from PoC to performance analysis to production
  • Perform deep analysis of AI workloads and frameworks to identify multi-GPU communication requirements and opportunities
  • Collaborate hands-on with teams working on the latest AI models
  • Improve AI compilers to hide communications or perform automatic fusion
  • Conduct in-depth AI workload performance characterization on multi-GPU clusters
  • Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads
  • Author custom communication or fused compute-communication kernels to showcase ultimate performance on NV platforms
  • Influence the roadmap of communication libraries
  • NCCL & NVSHMEM
  • Collaborate with a very dynamic team across multiple time zones

Qualifications

  • B.S, M.S. or PHD in Computer Science, or related field (or equivalent experience) with 5+ software engineering and HPC/AI experience
  • Development or integration experience with Deep Learning Frameworks such Py Torch, JAX, and Inference Engines such as TRT-LLM, vLLM, SGLang
  • Rapid prototyping and development with Python, C++, CUDA or related DSLs (Triton, cu Te)
  • Solid grasp of AI models, parallelisms, and/or compiler technologies (e.g. torch.compile)
  • Experience conducting performance benchmarking on AI clusters
  • Familiarity with at least one performance profiler toolchain (Py Torch profiler, NVIDIA Nsight Systems)
  • Understanding of HPC/AI communication concepts (1-sided v 2-sided communication, elasticity, resiliency, topology discovery, etc)
  • Adaptability and passion to learn new areas and tools
  • Flexibility to work and communicate effectively across different teams and timezones

Ways to Stand Out from the Crowd

  • Experience with parallel programming on at least one communication runtime (NCCL, NVSHMEM, MPI)
  • Good understanding of computer system architecture, HW-SW interactions and operating systems principles (aka systems software fundamentals)
  • Expertise in one or more of these areas: Training, Distributed inference, MoE, Reinforcement Learning, kernel authoring (on CUDA, Triton, cu Te, etc)
  • Experience with programming for compute & communication overlap in distributed runtimes
  • Experience with AI compiler pattern matching and lowering
  • Solid understanding of memory hierarchy, consistency model, and tensor layout

Compensation and Benefits

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits.

Application Information

Applications for this job will be accepted at least until February 15, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.

Equal Opportunity

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. NVIDIA is the world leader in accelerated computing. NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society. Learn more about NVIDIA.

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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