Senior DGX Cloud Performance Engineer at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Cloud Computing, TechnologyIndustries

Requirements

12+ years of proven experience
Ability to work with large scale parallel and distributed accelerator-based systems
Expertise optimizing performance and AI workloads on large scale systems
Experience with performance modeling and benchmarking at scale
Strong background in Computer Architecture, Networking, Storage systems, Accelerators
Familiarity with popular AI frameworks (PyTorch, TensorFlow, JAX, Megatron-LM, Tensort-LLM, VLLM) among others
Experience with AI/ML models and workloads, in particular LLMs
Understanding of DNNs and their use in emerging AI/ML applications and services
Bachelors or Masters in Engineering (preferably, Electrical Engineering, Computer Engineering, or Computer Science) or equivalent experience
Proficiency in Python, C/C++
Expertise with at least one of public CSP infrastructure (GCP, AWS, Azure, OCI, …)

Responsibilities

Develop benchmarks, end to end customer applications running at scale, instrumented for performance measurements, tracking, sampling, to measure and optimize performance of meaningful applications and services
Construct carefully designed experiments to analyze, study and develop critical insights into performance bottlenecks, dependencies, from an end to end perspective
Develop ideas on how to improve the end to end system performance and usability by leading changes in the HW or SW (or both)
Collaborate with external CSPs during the full life cycle of cluster deployment and workload optimization to understand and drive standard methodologies
Collaborate with AI researchers, developers, and application service providers to understand difficulties, requirements, project future needs and share best practices
Work with a diverse set of LLM workloads and their application areas such as health care, climate modeling, pharmaceuticals, financial futures, Genomics/Drug discovery, among others
Develop the vital modeling framework and the TCO analysis to enable efficient exploration and sweep of the architecture and design space
Develop the methodology needed to drive the engineering analysis to advise the architecture, design and roadmap of DGX Cloud

Skills

Distributed Systems

Parallel Systems

Performance Analysis

Benchmark Development

Performance Optimization

AI Workloads

LLM Workloads

HW-SW Co-design

Cluster Architecture

NVIDIA DGX

Performance Profiling

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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