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Deep Learning Accelerator Jobs (NOW HIRING)

NVIDIA Corporation

$90K - $119K/yr

## Senior Deep Learning Software Engineer, InferenceApplylocations: US, CA, Remote: US, TX, Remote: US ... Scale performance of DL models across different architectures and types of NVIDIA accelerators.

Nvidia

$122K - $161K/yr

Scale performance of DL models across different architectures and types of NVIDIA accelerators ... Contribute to deep learning software projects, such as PyTorch, vLLM, and SGLang to drive ...

Nvidia

Senior Deep Learning Software Engineer, Inference

OR · On-site +1

$122K - $161K/yr

Scale performance of DL models across different architectures and types of NVIDIA accelerators ... Ways to Stand out from The Crowd Contribute to deep learning software projects, such as PyTorch ...

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Deep Learning Accelerator information

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$11K

$83.9K

$140K

How much do deep learning accelerator jobs pay per year?

As of Sep 11, 2026, the average yearly pay for deep learning accelerator 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 a deep learning accelerator?

Deep Learning Accelerators are specialized hardware or systems designed to speed up the processing and training of deep learning algorithms, such as neural networks. They are optimized for the heavy computational demands of tasks like image recognition, natural language processing, and other AI applications. Examples include Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and custom-designed chips like Application-Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs). These accelerators enable faster data processing, lower power consumption, and improved efficiency compared to general-purpose CPUs. As AI applications grow, the use of deep learning accelerators is becoming increasingly important in both research and industry.

What skills and qualifications are needed to thrive as a deep learning accelerator engineer?

To thrive as a Deep Learning Accelerator Engineer, you need a strong background in computer engineering, digital design, and machine learning, typically supported by a degree in computer science or electrical engineering. Experience with hardware description languages (such as Verilog or VHDL), FPGA/ASIC toolchains, and familiarity with deep learning frameworks like TensorFlow or PyTorch is essential. Problem-solving, teamwork, and effective communication are crucial soft skills for collaborating with cross-functional teams and translating algorithmic requirements into efficient hardware solutions. These skills are vital to designing high-performance, energy-efficient hardware accelerators that advance AI capabilities and meet industry demands.

What are the main challenges faced when optimizing deep learning models for hardware accelerators?

One of the primary challenges in this role is bridging the gap between deep learning model requirements and the constraints of specialized hardware, such as GPUs, TPUs, or custom ASICs. This often involves model quantization, memory optimization, and adapting algorithms to exploit hardware parallelism while maintaining accuracy and efficiency. Collaboration with both hardware engineers and software developers is essential to ensure models run efficiently on target platforms, and staying current with evolving accelerator architectures is key to long-term success.

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

AspectDeep Learning AcceleratorMachine Learning Engineer
Required CredentialsKnowledge of hardware design, FPGA/ASIC programming, deep learning frameworksDegree in Computer Science, Data Science, or related fields; experience with ML frameworks
Work EnvironmentHardware development labs, embedded systems, AI hardware companiesSoftware development environments, tech companies, research labs
Industry UsageAI hardware manufacturing, embedded AI solutionsAI/ML software development, data analysis, model deployment
Search & Comparison IntentFocus on hardware acceleration, AI hardware designFocus on software development, model building

Deep Learning Accelerators specialize in hardware design and optimization for AI workloads, working closely with hardware and embedded systems. Machine Learning Engineers develop and deploy ML models primarily through software, focusing on algorithms and data. While both roles involve AI, their core skills, work environments, and industry applications differ significantly.

What cities are hiring for Deep Learning Accelerator jobs?

Cities with the most Deep Learning Accelerator job openings:

What states have the most Deep Learning Accelerator jobs?

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What are popular job titles related to Deep Learning Accelerator jobs?

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Deep Learning Accelerator jobs near you
Infographic showing various Deep Learning Accelerator job openings in the United States as of June 2026, with employment types broken down into 4% Internship, 8% As Needed, 80% Full Time, 4% Part Time, and 4% Summer. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.

NVIDIA 2027 Internships: Deep Learning

Santa Clara, CA • On-site

Nvidia Corporation
Computer and Electronic Product Manufacturing • 10K+ employees
Learn more about Nvidia Corporationexternal

Full-time

Posted 23 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

Job description

By submitting your resume, you acknowledge that your 2027 Deep Learning internship application will be processed in accordance with NVIDIA's Applicant Privacy Policy and you agree to our Terms of Service. We'll review resumes on an ongoing basis, and a recruiter may reach out if your experience fits one of our many internship opportunities.
NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society - from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create. Our internships offer an excellent opportunity to expand your career and get hands on experience with one of our industry leading Deep Learning teams. We're seeking strategic, ambitious, hard-working, and creative individuals who are passionate about helping us tackle challenges no one else can solve.
Throughout the 12-week full-time internship, students will work on projects that have a measurable impact on our business. We're looking for students pursuing a B.S., M.S., or Ph.D. degree within a relevant or related field.
Potential Internships in this field include:
Deep Learning Applications and Algorithms
  • Developing algorithms for deep learning, data analytics, or scientific computing to improving performance of GPU implementations
  • Course or internship experience related to the following areas could be required: Deep Neural Networks, Linear Algebra, Numerical Methods and/or Computer Vision, Software Design, Computer Memory (Disk, Memory, Caches), CPU and GPU Architectures, Networking, Numeric Libraries, Embedded System Design and Development, Drivers, Real-Time Software

Deep Learning Frameworks and Libraries
  • Building underlying frameworks and libraries to accelerate Deep Learning on GPUs
  • Contributing directly to software packages such as JAX, PyTorch, and TensorFlow, integrating the latest library (e.g., cuDNN) or CUDA features, performance tuning, and analysis
  • Optimizing core deep learning algorithms and libraries (e.g., CuDNN, CuBLAS), maintaining build, test, and distribution infrastructure for these libraries and deep learning frameworks on NVIDIA supported platforms
  • Course or internship experience related to the following areas could be required: Computer Architecture (CPUs, GPUs, FPGAs or other accelerators), GPU Programming Models, Performance-Oriented Parallel Programming, Optimizing for High-Performance Computing (HPC), Algorithms, Numerical Methods

What we need to see:
  • Must be actively enrolled in a university pursuing a B.S., M.S., or Ph.D. degree in Electrical Engineering, Computer Engineering, or a related field, for the full duration of the internship; anticipated graduation date (month and year) must be clearly indicated on a resume or CV to be considered.
  • Depending on the internship role, prior experience or knowledge requirements could include the following programming skills and technologies: C, C++, CUDA, Python, x86, ARM CPU, GPU, Linux, Direct3D, Vulkan, OpenGL, OpenCL, Spark, Perl, Bash/Shell Scripting, Container Tools (Docker/Containers, Kubernetes), Infrastructure Platforms (AWS, Azure, GCP), Data Technologies (Kafka, ELK, Cassandra, Apache Spark), React, Go

Click hereto learn more about NVIDIA, our early talent programs, benefits offered to students and other helpful student resources related to our latest technologies and endeavors.
Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 20 USD - 71 USD.
You will also be eligible for Intern benefits.
Applications are accepted on an ongoing basis.
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.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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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

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