Solutions Architect, Applied AI 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, TechnologyIndustries

Requirements

Strong foundational expertise, from a BS, MS, or Ph.D. degree in Engineering, Mathematics, Physics, Computer Science, Data Science, or similar (or equivalent experience)
5+ years experience using deep learning frameworks and libraries such as PyTorch, Tensorflow/Keras, Hugging Face Transformers, Megatron-LM, and DeepSpeed
Expertise running deep learning jobs on GPUs using SLURM and Kubernetes
Demonstrated coding and debugging skills, including 5+ years experience with Python and Linux
Hands-on experience with customizing AI models, including distillation, pre-training, supervised finetuning, reinforcement learning, reasoning, evaluation, guard railing, and data curation
Demonstrated expertise in accuracy and performance profiling and optimization for AI training and inference workloads
Ability to learn fast and quickly adapt to change
Clear written and oral communications skills with the ability to effectively collaborate with executives and engineering teams

Responsibilities

Develop end-to-end AI solutions for enterprise use cases
Help customers adopt NVIDIA AI SDKs and APIs by offering deep technical expertise and designing GPU-accelerated pipelines that optimize compute resource utilization and improve workload performance
Solve customer problems by building solutions using deep learning technology including language and multimodal models, information retrieval, domain customization, reinforcement learning, reasoning, inferencing, agentic systems, and other sophisticated AI workloads
Build reference architectures needed to deploy and optimize workloads at large scale across multiple industries
Help improve NVIDIA products and build creative solutions to overcome scaling challenges
Contribute to the wider organization and community by sharing expert knowledge, such as product engineering contributions and building/delivering hands-on training
Work closely with customers and partners to customize and deploy AI workloads in production at scale

Skills

Key technologies and capabilities for this role

Deep LearningGenerative AIGPU ComputingNVIDIA AI SDKsCloud ComputingLanguage ModelsMultimodal ModelsInformation RetrievalReinforcement LearningInferencingAgentic Systems

Questions & Answers

Common questions about this position

What education and experience are required for this Solutions Architect role?

A BS, MS, or Ph.D. in Engineering, Mathematics, Physics, Computer Science, Data Science, or similar (or equivalent experience) is required, along with 5+ years experience using deep learning frameworks like PyTorch, Tensorflow/Keras, Hugging Face Transformers, Megatron-LM, and DeepSpeed.

What technical skills are essential for this position?

Expertise running deep learning jobs on GPUs using SLURM and Kubernetes, demonstrated coding and debugging skills with 5+ years experience in Python and Linux, and hands-on experience with customizing AI models including distillation, pre-training, supervised finetuning, and reinforcement learning are essential.

What is the salary or compensation for this role?

This information is not specified in the job description.

Is this position remote or does it require office work?

This information is not specified in the job description.

What does the team culture look like at NVIDIA's Applied AI SA Segment?

The team specializes in the newest technology in deep learning, Generative AI, and Cloud, working closely with customers, partners, other Solution Architects, Product, Engineering, and Research teams to develop end-to-end AI solutions and share expert knowledge through contributions and training.

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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