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Nvidia Llm Performance Jobs (NOW HIRING)

Nvidia

OR · Hybrid

NVIDIA seeks a Senior DL Performance Architect to join our group of pioneers who enjoy pushing AI ... If you are passionate about AI efficiency Pareto curves, have a proven record of modeling LLM ...

Nvidia

AI Inference Performance Engineer

Santa Clara, CA · Hybrid

$164K/yr

... NVIDIA's latest accelerators, defining the industry's performance standards across language models, video generation, and speech workloads. We work directly within TensorRT-LLM, SGLang, and vLLM ...

Nvidia

OR

$139K/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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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 Deep Learning Software Engineer, LLM Performance

Senior Deep Learning Software Engineer, LLM Performance

Nvidia

Santa Clara, CA • Hybrid

$164K/yr

Full-time

Posted 23 days ago

Job description

We are now looking for a Senior Deep Learning Software Engineer, LLM Performance!NVIDIA is seeking an experienced Deep Learning Engineer passionate about analyzing and improving the performance of LLM inference! NVIDIA is rapidly growing our research and development for Deep Learning Inference and is seeking excellent Software Engineers at all levels of expertise to join our team. Companies around the world are using NVIDIA GPUs to power a revolution in deep learning, enabling breakthroughs in areas like LLM, Generative AI, Recommenders and Vision that have put DL into every software solution. Join the team that builds the software to enable the performance optimization, deployment and serving of these DL solutions. We specialize in developing GPU-accelerated Deep learning software like TensorRT, DL benchmarking software and performant solutions to deploy and serve these models.

Collaborate with the deep learning community to implement the latest algorithms for public release in TensorRT LLM, VLLM, SGLang and LLM benchmarks. Identify performance opportunities and optimize SoTA LLM models across the spectrum of NVIDIA accelerators, from datacenter GPUs to edge SoCs. Implement LLM inference, serving and deployment algorithms and optimizations using TensorRT LLM, VLLM, SGLang, Triton and CUDA kernels. Work and collaborate with a diverse set of teams involving performance modeling, performance analysis, kernel development and inference software development.

What you'll be doing:

  • Performance optimization, analysis, and tuning of LLM, VLM and GenAI models for DL inference, serving and deployment in NVIDIA/OSS LLM frameworks.

  • Scale performance of LLM models across different architectures and types of NVIDIA accelerators.

  • Scale performance for max throughput, minimum latency and throughput under latency constraints.

  • Contribute features and code to NVIDIA/OSS LLM frameworks, inference benchmarking frameworks, TensorRT, and Triton.

  • Work with cross-collaborative teams across generative AI, automotive, image understanding, and speech understanding to develop innovative solutions.

What we need to see:

  • Bachelors, Masters, PhD, or equivalent experience in relevant fields (Computer Engineering, Computer Science, EECS, AI).

  • At least 8 years of relevant software development experience.

  • Excellent Python/C/C++ programming, software design and software engineering skills

  • Experience with a DL framework like PyTorch, JAX, TensorFlow.

Ways to stand out from the crowd:

  • Prior experience with a LLM framework or a DL compiler in inference, deployment, algorithms, or implementation

  • Prior experience with performance modeling, profiling, debug, and code optimization of a DL/HPC/high-performance application

  • Architectural knowledge of CPU and GPU

  • GPU programming experience (CUDA or OpenCL)

GPU deep learning has provided the foundation for machines to learn, perceive, reason and solve problems posed using human language. The GPU started out as the engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA's GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for deep learning, and NVIDIA is increasingly known as "the AI computing company." Come, join our DL Architecture team, where you can help build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field.

#LI-Hybrid

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 April 20, 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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