Senior Deep Learning Communication Architect at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, High Performance Computing, TechnologyIndustries

Requirements

Ph.D., Masters, or BS in Computer Science (CS), Electrical Engineering (EE), Computer Science and Electrical Engineering (CSEE), or a closely related field or equivalent experience
6+ years of experience in Building DNNs, Scaling of DNNs, Parallelism of DNN frameworks, or deep learning training and inference workloads
Experience in evaluating, analyzing, and optimizing LLM training and inference performance of state-of-the-art models on cutting-edge hardware
Deep understanding of parallelism techniques, including Data Parallelism, Pipeline Parallelism, Tensor Parallelism, Expert Parallelism, and FSDP
Understanding of the emerging serving architectures like Disaggregated Serving and inference servers like Dynamo and Triton
Proficiency in developing code for one or more deep neural network (DNN) training and Inference frameworks, such as PyTorch, TensorRT-LLM, vLLM, SGLang
Strong programming skills in C++ and Python
Familiarity with GPU computing, including CUDA and OpenCL, and familiarity with InfiniBand and RoCE networks

Responsibilities

Optimizing communication performance: Identify and eliminate bottlenecks in data transfer and synchronization during distributed deep learning training and inference
Designing efficient communication protocols: Develop and implement communication algorithms and protocols tailored for deep learning workloads, minimizing communication overhead and latency
Hardware and software co-craft: Collaborate with hardware and software teams to craft systems that effectively apply high-speed interconnects (e.g., NVLink, InfiniBand, SPC-X) and communication libraries (e.g., MPI, NCCL, UCX, UCC, NVSHMEM)
Exploring innovative communication technologies: Research and evaluate new communication technologies and techniques to enhance the performance and scalability of deep learning systems
Developing and implementing solutions: Build proofs-of-concept, conduct experiments, and perform quantitative modeling to validate and deploy new communication strategies

Skills

Key technologies and capabilities for this role

Deep LearningDNNDistributed TrainingNVLinkInfiniBandSPC-XMPINCCLUCXUCCNVSHMEMCommunication ProtocolsHardware Software Co-design

Questions & Answers

Common questions about this position

What education and experience are required for this role?

A Ph.D., Masters, or BS in Computer Science, Electrical Engineering, or related field is required, along with 6+ years of experience in building and scaling DNNs, parallelism in DNN frameworks, or deep learning training and inference workloads.

What technical skills are essential for this position?

Key skills include deep understanding of parallelism techniques like Data Parallelism, Pipeline Parallelism, Tensor Parallelism, Expert Parallelism, and FSDP; proficiency in C++ and Python; and familiarity with GPU computing (CUDA, OpenCL), InfiniBand, RoCE, and DNN frameworks like PyTorch and TensorRT-LLM.

What is the salary for this Senior Deep Learning Communication Architect role?

This information is not specified in the job description.

Is this position remote or onsite, and where is it located?

This information is not specified in the job description.

What is the company culture like at NVIDIA?

NVIDIA offers a diverse, supportive environment where everyone is inspired to do their best work, fueled by innovation and great people.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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