Senior AI Performance and Efficiency Engineer at NVIDIA

Shanghai, China

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, AI/MLIndustries

Requirements

BS or similar background in Computer Science or related area (or equivalent experience)
Minimum 8+ years of experience designing and operating large scale compute infrastructure
Strong understanding of modern ML techniques and tools
Experience investigating, and resolving, training & inference performance end to end
Debugging and optimization experience with NSight Systems and NSight Compute
Experience with debugging large-scale distributed training using NCCL
Proficiency in programming & scripting languages such as Python, Go, Bash, as well as familiarity with cloud computing platforms (e.g., AWS, GCP, Azure) in addition to experience with parallel computing frameworks and paradigms
Dedication to ongoing learning and staying updated on new technologies and innovative methods in the AI/ML infrastructure sector
Excellent communication and collaboration skills, with the ability to work effectively with teams and individuals of different backgrounds
Ways to stand out (Preferred)
Background with NVIDIA GPUs, CUDA Programming, NCCL and MLPerf benchmarking
Experience with Machine Learning and Deep Learning concepts, algorithms and models
Familiarity with InfiniBand with IBOP and RDMA
Understanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloads
Familiarity with deep learning frameworks like PyTorch and TensorFlow

Responsibilities

Collaborate closely with our AI/ML researchers to make their ML models more efficient leading to significant productivity improvements and cost savings
Build tools, frameworks, and apply ML techniques to detect & analyze efficiency bottlenecks and deliver productivity improvements for our researchers
Work with researchers working on a variety of innovative ML workloads across Robotics, Autonomous vehicles, LLM’s, Videos and more
Collaborate across the engineering organizations to deliver efficiency in our usage of hardware, software, and infrastructure
Proactively monitor fleet wide utilization patterns, analyze existing inefficiency patterns, or discover new patterns, and deliver scalable solutions to solve them
Keep up to date with the most recent developments in AI/ML technologies, frameworks, and successful strategies, and advocate for their integration within the organization

Skills

AI/ML

GPU Clusters

ML Techniques

NSight Systems

NSight Compute

Distributed Training

Performance Optimization

Compute Infrastructure

LLM

Robotics

Autonomous Vehicles

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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