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Nvidia Llm Performance Jobs (NOW HIRING)

Nvidia

OR · Hybrid

$122K - $161K/yr

Join NVIDIA's TensorRT Edge-LLM team and help shape the next generation of edge AI for automotive ... Benchmark, profile, and optimize inference performance across diverse embedded and automotive ...

Nvidia

OR · Hybrid

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than ... Work on critical designs which are integral to our LLM performance such as real-time numeric ...

Nvidia

OR

$122K - $161K/yr

We are looking for a Senior DL Algorithms Engineer for LLM/Omni model optimizations! Seeking senior ... Enable and optimize state-of-the-art open models (like Nemotron and Cosmos) on NVIDIA's accelerated ...

Nvidia

OR

$122K - $161K/yr

Contribute new features, fix bugs and deliver production code to TRT-LLM, NVIDIA's open-source ... Experience with performance profiling, analysis and optimization, especially for GPU-based ...

Nvidia

OR · Hybrid

$122K - $161K/yr

NVIDIA is seeking an experienced Deep Learning Engineer passionate about analyzing and improving ... TensorRT, TensorRT-LLM, vLLM, SGLang, FlashInfer). * Experience with performance analysis and ...

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Nvidia Llm Performance information

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$40K

$99.5K

$153.5K

How much do nvidia llm performance jobs pay per year?

As of Jun 6, 2026, the average yearly pay for nvidia llm performance in the United States is $99,528.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,500.00 and $126,000.00 per year, depending on experience, location, and employer.

What does a Nvidia LLM Performance Engineer do?

A Nvidia LLM (Large Language Model) Performance Engineer focuses on optimizing the speed, efficiency, and scalability of large language models running on Nvidia hardware and software platforms. They analyze performance bottlenecks, implement improvements, and work closely with AI framework and hardware teams to maximize throughput and minimize latency. Their work ensures that AI models can process data more efficiently, making them suitable for real-world deployment in applications like chatbots, virtual assistants, and more.

What are the key skills and qualifications needed to thrive as an NVIDIA LLM Performance Engineer, and why are they important?

To excel as an NVIDIA LLM Performance Engineer, you typically need a strong background in computer science, machine learning, and performance optimization, often supported by experience with large language models and advanced degree(s) in related fields. Proficiency with CUDA, Python, deep learning frameworks (like PyTorch or TensorFlow), and performance profiling tools is crucial. Analytical thinking, problem-solving, and effective communication are vital soft skills for collaborating with cross-functional teams and translating technical findings. These abilities enable engineers to optimize LLM deployment on NVIDIA hardware, ensuring cutting-edge AI solutions run efficiently and at scale.

What are some common challenges faced when optimizing large language model (LLM) performance on Nvidia hardware?

One of the main challenges in this role is balancing computational efficiency with the accuracy and scalability of large language models. Optimizing LLMs for Nvidia GPUs often requires deep knowledge of parallel computing, memory management, and the specific features of Nvidia's hardware and software stack. Additionally, you'll need to collaborate closely with data scientists, ML engineers, and hardware specialists to troubleshoot bottlenecks and deploy models efficiently in production environments. Staying up-to-date with the latest frameworks (like CUDA, TensorRT, and cuDNN) and best practices is essential to succeed in this dynamic and fast-evolving field.

What is the difference between Nvidia Llm Performance vs Data Scientist?

AspectNvidia Llm PerformanceData Scientist
Required CredentialsTechnical certifications in AI/ML, GPU computingDegree in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, AI development teams, hardware-focused settingsBusiness environments, analytics teams, research institutions
Industry UsageAI hardware optimization, deep learning model deploymentData analysis, predictive modeling, data visualization

While Nvidia Llm Performance focuses on optimizing large language models and AI hardware performance, Data Scientists analyze data to generate insights and build predictive models. Both roles require technical expertise but differ in their core focus: hardware and model performance versus data analysis and interpretation.

Infographic showing various Nvidia Llm Performance job openings in the United States as of May 2026, with employment types broken down into 33% Full Time, and 67% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $99,528 per year, or $47.9 per hour.
Senior Solutions Architect, GPU Performance and LLM - Cloud Service Providers

Senior Solutions Architect, GPU Performance and LLM - Cloud Service Providers

Nvidia Corporation

Santa Clara, CA • On-site

Full-time

Posted 22 days ago

Job description

Join our team at NVIDIA and help bring AI solutions to our largest customers. We are seeking an expert Solutions Architect to assist customers in building AI/ML and HPC software solutions at scale. As a member of our Solutions Architecture team, you will collaborate with strategic customers, providing end-to-end technology solutions and technical support based on our product strategy. Come join us!
What you'll be doing:
  • Working with tech giants to develop and demonstrate solutions based on NVIDIA's groundbreaking software and hardware technologies.
  • Partnering with Sales Account Managers and Developer Relations Managers to identify and secure business opportunities for NVIDIA products and solutions.
  • Serving as the main technical point of contact for customers engaged in the development of intricate AI infrastructure, while also offering support in understanding performance aspects related to tasks like large scale LLM training and inference.
  • Conducting regular technical customer meetings for project/product details, feature discussions, introductions to new technologies, performance advice, and debugging sessions.
  • Collaborating with customers to build Proof of Concepts (PoCs) for solutions to address critical business needs and support cloud service integration for NVIDIA technology on hyperscalers.
  • Analyzing and developing solutions for customer performance issues for both AI and systems performance.

What we need to see:
  • BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields or equivalent experience.
  • 8+ years of engineering (performance/system/solution) experience.
  • Hands-on experience building performance benchmarks for data center systems, including large scale AI training and inference.
  • Understanding of systems architecture including AI accelerators and networking as it relates to the performance of an overall application.
  • Effective engineering program management with the capability of balancing multiple tasks.
  • Ability to communicate ideas clearly through documents, presentations, and in external customer-facing environments.

Ways to stand out from the crowd:
  • Hands-on experience with Deep Learning frameworks (PyTorch, JAX, etc.), compilers (Triton, XLA, etc.), and NVIDIA libraries (TRTLLM, TensorRT, Nemo, NCCL, RAPIDS, etc.).
  • Familiarity with deep learning architectures and the latest LLM developments.
  • Background with NVIDIA hardware and software, performance tuning, and error diagnostics.
  • Hands-on experience with GPU systems in general including but not limited to performance testing, performance tuning, and benchmarking.
  • Experience deploying solutions in cloud environments including AWS, GCP, Azure, or OCI as well as knowledge of DevOps/MLOps technologies such as Docker/containers, Kubernetes, data center deployments, etc. Command line proficiency.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until May 12, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
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.
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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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