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Deep Learning Researcher Jobs (NOW HIRING)

Nvidia

Senior Deep Learning Engineer

Redmond, WA

$62 - $79.75/hr

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 ...

Nvidia

Senior Deep Learning Engineer

Santa Clara, CA

$65 - $83.75/hr

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 ...

PlusAI

Deep Learning Research Intern

Santa Clara, CA · On-site

$19 - $65/hr

... deep learning framework (PyTorch, Tensorflow, Jax) $19 - $65 an hour Your opportunities joining PlusAI Work, learn and grow in a highly future-oriented, innovative and dynamic field. Wide range of ...

PlusAI

Deep Learning Research Intern

Santa Clara, CA

$19 - $65/hr

... deep learning framework (PyTorch, Tensorflow, Jax) $19 - $65 an hour Your opportunities joining PlusAI Work, learn and grow in a highly future-oriented, innovative and dynamic field. Wide range of ...

IMC

Machine Learning Researcher

Chicago, IL

IMC Trading is seeking quantitative researchers with a proven track record to apply state-of-the-art machine learning & deep learning to solve challenging trading problems. This role is part of a ...

Matroid

Deep Learning Engineer

Palo Alto, CA · On-site

$170K - $300K/yr

Keep on top of the latest developments and research in academic CV/DL and decide how we should ... Passion for computer vision and deep learning; you are excited to adapt the latest multimodal LLMs ...

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All JobsDeep Learning Researcher Jobs

Deep Learning Researcher information

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$30K

$113.1K

$164.5K

How much do deep learning researcher jobs pay per year?

As of Jun 26, 2026, the average yearly pay for deep learning researcher in the United States is $113,102.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,000.00 and $154,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by Deep Learning Researchers when transitioning models from research to production environments?

Deep Learning Researchers often encounter challenges such as ensuring model robustness, addressing scalability issues, and optimizing computational efficiency when moving models from research to production. Real-world data can be noisier or more variable than research datasets, requiring additional data preprocessing and validation. Collaboration with engineering teams is crucial to integrate models effectively, and researchers may need to adapt their algorithms to meet latency and resource constraints of production environments.

What is a Deep Learning Researcher?

A Deep Learning Researcher is a specialist who develops and investigates advanced algorithms and models that allow computers to learn from large amounts of data, particularly using artificial neural networks. They focus on designing, training, and evaluating deep learning models to solve complex problems in areas like computer vision, natural language processing, and speech recognition. Their work often involves staying up to date with the latest advancements in machine learning and contributing to research publications. Deep Learning Researchers may work in academia, industry, or research labs, collaborating with other scientists and engineers to push the field forward.

What are the key skills and qualifications needed to thrive as a Deep Learning Researcher, and why are they important?

To thrive as a Deep Learning Researcher, you need a strong background in computer science, mathematics, and machine learning principles, typically supported by an advanced degree (Master’s or PhD) in a related field. Proficiency with frameworks like TensorFlow or PyTorch, experience with high-performance computing, and familiarity with relevant programming languages such as Python are essential. Strong problem-solving, collaboration, and communication skills help you innovate and effectively share research findings. These skills and qualities are crucial for advancing state-of-the-art models and driving impactful AI solutions.

What is the difference between Deep Learning Researcher vs Machine Learning Engineer?

AspectDeep Learning ResearcherMachine Learning Engineer
CredentialsAdvanced degrees in AI, CS, or related fields; research publicationsDegree in CS, Data Science, or related; practical experience
Work EnvironmentResearch labs, academia, R&D departmentsIndustry, product teams, deployment environments
Employer & Industry UsageUniversities, research institutes, tech companiesTech companies, startups, enterprise firms
Primary FocusDeveloping new algorithms, theoretical advancementsImplementing models, deploying solutions, optimizing systems

While both roles involve working with machine learning models, Deep Learning Researchers focus on advancing the theoretical foundations and developing new algorithms, often in research settings. Machine Learning Engineers apply these models in real-world applications, focusing on deployment and system optimization.

More about Deep Learning Researcher jobs
What job categories do people searching Deep Learning Researcher jobs look for? The top searched job categories for Deep Learning Researcher jobs are:
Deep Learning Researcher jobs near you
Infographic showing various Deep Learning Researcher job openings in the United States as of June 2026, with employment types broken down into 4% Full Time, 87% Part Time, and 9% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $113,102 per year, or $54.4 per hour.
Senior Deep Learning Engineer

Senior Deep Learning Engineer

Nvidia

Redmond, WA

$62 - $79.75/hr

Full-time

Posted 25 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 areassuch 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.
Nvidia logo

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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