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Nvidia Deep Learning Jobs (NOW HIRING)

Nvidia Corporation

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 ...

Nvidia

Senior Deep Learning Compiler Engineer

Austin, TX

$103K - $142K/yr

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than ... NVIDIA is hiring software engineers for its Deep Learning Compiler (DLC) team. Academic and ...

Nvidia

OR · On-site

$104K - $143K/yr

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than ... NVIDIA is hiring software engineers for its Deep Learning Compiler (DLC) team. Academic and ...

Nvidia

Senior Deep Learning Compiler Engineer

Redmond, WA

$117K - $160K/yr

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than ... NVIDIA is hiring software engineers for its Deep Learning Compiler (DLC) team. Academic and ...

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How much do nvidia deep learning jobs pay per year?

As of Jun 10, 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.

How much does a NVIDIA deep learning performance architect make?

A NVIDIA deep learning performance architect typically earns between $120,000 and $180,000 annually, depending on experience, location, and specific responsibilities. The role often requires expertise in AI frameworks, GPU architecture, and performance optimization. Compensation may also include bonuses and stock options based on performance and company policies.

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.

Is ML a high paying job?

Machine learning (ML) roles, including those related to Nvidia deep learning, are generally well-paid due to high demand for specialized skills in AI, data analysis, and programming. Salaries vary based on experience, location, and certifications, but many ML positions offer competitive compensation compared to other tech roles.

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.

How much does a deep learning engineer make at NVIDIA?

A deep learning engineer at NVIDIA typically earns between $100,000 and $150,000 annually, depending on experience, location, and skill level. Senior roles or those with specialized expertise in AI and GPU programming can earn higher salaries, often exceeding $180,000. Compensation may also include bonuses and stock options.

How hard is it to get hired at NVIDIA?

Getting hired at NVIDIA for deep learning roles can be competitive, often requiring strong technical skills in machine learning, deep learning frameworks, and programming languages like Python and C++. Candidates typically need relevant experience, a solid educational background, and a demonstrated ability to work on complex projects. The hiring process may include technical interviews, coding assessments, and behavioral evaluations.

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.

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Nvidia Deep Learning jobs near you
Infographic showing various Nvidia Deep Learning job openings in the United States as of June 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $83,885 per year, or $40.3 per hour.
Senior Deep Learning Engineer

Senior Deep Learning Engineer

Nvidia Corporation

Redmond, WA • On-site

$62 - $79.75/hr

Full-time

Posted 8 days ago

Job description

We are now looking for a Senior Deep Learning Engineer! 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 development of next-generation inference optimizations targeting frontier workloads including multi-agent AI systems, generative multimodal models, and inference-time compute scaling. In this role, you will characterize these emerging workloads and develop novel methods to optimize for them across inferencing engines, systems, and hardware architectures. Your work will span multiple tiers of the inference stack from the algorithmic to system level.
As NVIDIA makes significant strides in AI datacenters, our team holds a central role in maximizing the efficiency of our exponentially growing inference deployment needs and establishing a data-driven approach to algorithmic improvements, hardware design and system software development. We collaborate extensively with diverse teams at NVIDIA, spanning deep learning research and framework development teams, to silicon architecture. Thriving in such a high-impact, interdisciplinary environment necessitates not only technical proficiency but also a growth mindset and a pragmatic attitude - qualities that fuel our collective success in shaping the future of datacenter technology.
What You'll Be Doing:
  • Continuously keeping up to date on the latest advancements in generative AI research.
  • Analyzing and prototyping emerging workloads in multi-agent AI systems, generative multimodal models, and inference-time compute scaling.
  • Pioneering and developing optimizations for these workloads across the inference stack to push the boundaries of inferencing quality and speed on NVIDIA systems.
  • Collaborating closely with production teams to incorporate the latest advancements into cutting-edge software frameworks.

What We Need to See:
  • Master's degree (or equivalent experience) in Computer Science, Artificial Intelligence, Applied Mathematics, or related fields.
  • A strong foundation in deep learning, with a particular emphasis on generative models and inferencing.
  • A track record of at least 5 years of relevant software development experience in modern deep learning frameworks such as PyTorch.
  • Growth mindset and pragmatic attitude.

Ways to Stand Out From the Crowd:
  • Published research or noteworthy contributions to the field of deep learning, particularly in areas such as inference-time compute, multimodal generation, AI systems, etc.
  • Experience with prototyping or deployment of agentic AI systems and/or multimodal generation models.
  • Experience with collaborating across algorithms, software and performance teams to deliver high quality solutions.
  • Familiarity with computer architecture and how it relates to AI algorithms development.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. If you're creative and autonomous, we want to hear from you!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until May 31, 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

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