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Nvidia Deep Learning Jobs (NOW HIRING)

Senior Deep Learning Software Engineer

Redmond, WA · Hybrid

$137K - $180K/yr

Collaborate with teams across NVIDIA to use performant kernel implementations within the automated ... Familiarity with NVIDIA's deep learning SDKs such as TensorRT. * Prior experience in writing high ...

Senior Deep Learning Software Engineer

Santa Clara, CA · Hybrid

$143K - $189K/yr

Collaborate with teams across NVIDIA to use performant kernel implementations within the automated ... Familiarity with NVIDIA's deep learning SDKs such as TensorRT. * Prior experience in writing high ...

At NVIDIA, we are at the forefront of advancing the capabilities of artificial intelligence. We are seeking an ambitious and forward-thinking senior deep learning engineer to contribute to the ...

Senior Deep Learning Engineer

Redmond, WA

$62 - $79.75/hr

At NVIDIA, we are at the forefront of advancing the capabilities of artificial intelligence. We are seeking an ambitious and forward-thinking senior deep learning engineer to contribute to the ...

Senior Deep Learning Engineer

Redmond, WA · On-site

$62 - $79.75/hr

At NVIDIA, we are at the forefront of advancing the capabilities of artificial intelligence. We are seeking an ambitious and forward-thinking senior deep learning engineer to contribute to the ...

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Nvidia Deep Learning information

See salary details

$11K

$83.9K

$140K

How much do nvidia deep learning jobs pay per year?

As of Jul 9, 2026, the average yearly pay for nvidia deep learning in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 per year, depending on experience, location, and employer.

What is an Nvidia Deep Learning job?

An Nvidia Deep Learning job typically involves working with AI, machine learning, and deep learning technologies to develop, optimize, and deploy neural network models. Employees in these roles may work on GPU acceleration, AI frameworks like TensorFlow and PyTorch, and specialized hardware like NVIDIA GPUs and TensorRT. Positions can range from research scientists and software engineers to AI infrastructure specialists, focusing on improving model performance and scalability. These professionals contribute to cutting-edge AI applications in fields like autonomous vehicles, healthcare, and robotics.

What are the main challenges faced by professionals working in Nvidia Deep Learning roles?

Professionals in Nvidia Deep Learning positions often encounter challenges such as optimizing deep learning models to run efficiently on GPU architectures, keeping up with rapidly evolving AI frameworks, and troubleshooting complex system-level integration issues. They may also need to balance tight project deadlines with the demands of rigorous research and experimentation. Collaboration with interdisciplinary teams—such as software developers, data scientists, and hardware engineers—is common and essential to deliver robust solutions. Overcoming these challenges helps professionals stay at the forefront of innovation in the AI and deep learning industry.

What are the key skills and qualifications needed to thrive in the Nvidia Deep Learning position, and why are they important?

Excelling in an Nvidia Deep Learning role requires a strong background in computer science, machine learning, and mathematics, often supported by an advanced degree in a related field. Expertise in deep learning frameworks (such as TensorFlow or PyTorch), CUDA programming, and experience with Nvidia GPU hardware are typically expected, along with relevant certifications like Nvidia Deep Learning Institute credentials. Strong analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this position. These skills are crucial to efficiently develop, optimize, and deploy deep learning models leveraging Nvidia technologies in cutting-edge applications.

More about Nvidia Deep Learning jobs
What cities are hiring for Nvidia Deep Learning jobs? Cities with the most Nvidia Deep Learning job openings:
What are the most commonly searched types of Nvidia Deep Learning jobs? The most popular types of Nvidia Deep Learning jobs are:
What states have the most Nvidia Deep Learning jobs? States with the most job openings for Nvidia Deep Learning jobs include:
What job categories do people searching Nvidia Deep Learning jobs look for? The top searched job categories for Nvidia Deep Learning jobs are:
Infographic showing various Nvidia Deep Learning job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 25% Part Time, and 2% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.
Deep Learning Product Research Engineer - Product Innovation

Deep Learning Product Research Engineer - Product Innovation

Nvidia

Santa Clara, CA • On-site

Full-time

Posted 9 days ago


Job description

NVIDIA is at the center of the AI revolution. Our deep learning platforms, models, frameworks, and accelerated computing technologies help developers, researchers, and enterprises build the next generation of intelligent applications The Deep Learning Product Research team sits at the intersection of engineering, product, research, developer relations, and go-to-market. We help accelerate the path from cutting-edge AI research to real-world product adoption by building high-quality technical assets, proof-of-concept applications, benchmarks, white papers, and developer-facing materials that advance NVIDIA's generative AI platform We are looking for a hands-on engineer and generative AI practitioner who can build prototypes, write high-quality code, evaluate emerging technologies, explain sophisticated systems clearly, and turn research ideas into practical product capabilities. In this role, you will create prototypes, demos, white papers, benchmarks, blogs, sample applications, conference material, and other technical content. You will work closely with research, engineering, product, marketing, field teams, customers, and the developer community to identify opportunities, surface feedback, and improve products across NVIDIA's AI ecosystem!

What you'll be doing:

  • Build prototypes, proof-of-concept applications, benchmarks and technical demos to explore and showcase the art of possible with NVIDIA's generative AI platform. You will translate this work directly into high-quality into scalable demo artifacts, white papers, sample code, and other developer-facing materials.

  • Evaluate emerging trends in generative AI, including large language models, multimodal systems, agentic applications, model evaluation, inference optimization, and AI-assisted software development.

  • Collaborate closely with product managers, engineering teams, researchers, field teams, customers, and marketing partners to translate product capabilities into practical, developer-focused examples. Serve as the technical bridge, translating advanced AI capabilities and research concepts into practical, developer-focused product examples.

  • Evaluate the technical feasibility, scalability, and product relevance of emerging technologies. Synthesize deep technical insights, authoring decision memos and feature requests to inform internal roadmaps, drive integrations, and improve NVIDIA's software stack.

  • Present technical material through developer blogs, webinars, conferences, workshops, customer engagements, and community events.

  • Serve as a technical advocate for NVIDIA's deep learning platform, helping developers understand how to build, optimize, and deploy AI applications using NVIDIA technologies.

  • Stay current with advances in deep learning, generative AI, model training, fine-tuning, inference, optimization, deployment, agentic workflows, and the broader AI developer ecosystem.

What we need to see:

  • Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent experience.

  • 5+ years of meaningful experience in software engineering, machine learning engineering, AI engineering, solutions architecture, applied research, or a similar technical role.

  • Hands-on experience with machine learning, deep learning, or agentic AI, including building, training, fine-tuning, evaluating, deploying, or optimizing models and AI applications.

  • Practical experience with generative AI systems, including large language models, retrieval-augmented generation, agentic workflows, model evaluation, or AI application development.

  • Strong programming skills in Python, and experience with modern deep learning frameworks and libraries such as PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, TensorFlow, or similar tools.

  • Familiarity with modern AI-assisted development tools and coding agents such as Codex, Claude Code, Cursor, or similar systems.

  • Ability to create clear, accurate, technically thorough, and compelling content for developers, including tutorials, blogs, sample code, white papers, benchmarks, or demos.

  • Strong communication and presentation skills, with the ability to explain complex technical topics to both expert and non-expert audiences.

  • Ability to collaborate optimally across research, engineering, product, marketing, field, and customer-facing teams, and passion for applied AI research, technical storytelling, and improving the user experience for AI practitioners.

Ways to stand out from the crowd

  • PhD in Computer Science, Engineering, Machine Learning, Artificial Intelligence, or a related field.

  • 3+ years of hands-on experience with machine learning, deep learning, generative AI, large language models, multimodal models, reinforcement learning, model optimization, or agentic applications.

  • Experience building production-quality AI applications, developer tools or research prototypes.

  • Experience designing or evaluating agentic AI systems, AI coding assistants, model evaluation harnesses, RAG pipelines, synthetic data workflows, or AI safety workflows.

  • Experience with NVIDIA AI software, models, or frameworks such as NeMo, NeMo Retriever, NeMo Guardrails, NeMo RL, NIM, TensorRT, Dynamo, CUDA, cuDNN, or Nemotron models.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most brilliant, forward-thinking and hardworking people in the world working for us. There has never been a more exciting time to join!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 136,000 USD - 212,750 USD for Level 3, and 160,000 USD - 253,000 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 4, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive 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