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Internship Machine Learning Hardware Jobs (NOW HIRING)

IMC

Machine Learning Engineer

Chicago, IL · On-site

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the ... You'll collaborate with leading researchers, hardware experts, and software engineers to build ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

As part of our machine learning team, you will play a vital role in prototyping foundational machine learning tools that bridge the camera hardware and software, in order to build flawless camera ...

Constellation Space

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

Optimize algorithms for low-latency inference on edge devices (spacecraft hardware). * Collaborate ... Proven experience deploying machine learning models into production. * Strong software engineering ...

Qualcomm

Staff Engineer

San Diego, CA · On-site

... machine learning hardware (co-designed with machine learning software) for inference or training solutions. • Develops optimized software to enable AI models deployed on hardware (e.g., machine ...

Avride

Machine Learning Engineer

Austin, TX

Optimize inference performance, model compression, and deployment across various hardware platforms ... Strong understanding of fundamental machine learning algorithms and neural network techniques.

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... hardware is a significant plus. Company : Atoms is a robotics startup that develops industrial ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... hardware is a significant plus. Company : Atoms is a design company that specializes in the fields ...

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Internship Machine Learning Hardware information

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$25.5K

$42.6K

$88K

How much do internship machine learning hardware jobs pay per year?

As of Jul 4, 2026, the average yearly pay for internship machine learning hardware in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What is the difference between Internship Machine Learning Hardware vs Internship Data Scientist?

AspectInternship Machine Learning HardwareInternship Data Scientist
Required CredentialsBasic knowledge of hardware, electronics, and programmingStatistics, programming, and data analysis skills
Work EnvironmentHardware labs, electronics workshops, manufacturing settingsOffice, data analysis environments, cloud platforms
Employer & Industry UsageTech companies, hardware manufacturers, research labsTech firms, finance, healthcare, consulting
Common Search & Comparison IntentUnderstanding hardware-focused roles in ML projectsData analysis and modeling roles in ML

Internship Machine Learning Hardware focuses on developing and optimizing hardware components for ML systems, while Internship Data Scientist emphasizes analyzing data and building models. Both roles are essential in AI development but differ in skills, environment, and industry application.

What is an Internship in Machine Learning Hardware?

An Internship in Machine Learning Hardware is a temporary position for students or recent graduates to gain hands-on experience working with the physical components and systems that enable machine learning applications. Interns typically assist in designing, testing, and optimizing hardware such as GPUs, TPUs, or custom accelerators that run machine learning algorithms efficiently. This role often involves collaboration with software engineers and researchers to improve the performance and energy efficiency of machine learning models. The internship provides valuable exposure to both hardware engineering and the rapidly evolving field of artificial intelligence.

What are the key skills and qualifications needed to thrive as an Internship Machine Learning Hardware, and why are they important?

To thrive as an Internship Machine Learning Hardware, you need a solid foundation in computer engineering, electrical engineering, or computer science, with coursework or experience in machine learning and hardware design. Familiarity with hardware description languages (like Verilog or VHDL), Python, C++, and tools such as TensorFlow, PyTorch, or FPGA development environments is typically required. Strong problem-solving abilities, eagerness to learn, and effective teamwork and communication skills help interns excel in multidisciplinary environments. These competencies are crucial for contributing to hardware-accelerated machine learning solutions and collaborating efficiently with engineering teams.

What kinds of projects and responsibilities can I expect during an Internship in Machine Learning Hardware?

As an intern in Machine Learning Hardware, you can expect to work on tasks such as benchmarking hardware performance for AI workloads, supporting the development and testing of new accelerator architectures, and optimizing hardware-software integration for machine learning models. You'll often collaborate with both hardware engineers and machine learning researchers, gaining exposure to the entire workflow from design to deployment. These internships typically provide hands-on experience with tools like FPGA, ASIC simulation environments, or specialized ML hardware platforms, and offer opportunities to contribute to real-world product development and research.
More about Internship Machine Learning Hardware jobs
What cities are hiring for Internship Machine Learning Hardware jobs? Cities with the most Internship Machine Learning Hardware job openings:
What are the most commonly searched types of Machine Learning Hardware jobs? The most popular types of Machine Learning Hardware jobs are:
What states have the most Internship Machine Learning Hardware jobs? States with the most job openings for Internship Machine Learning Hardware jobs include:
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Internship Machine Learning Hardware jobs near you
Infographic showing various Internship Machine Learning Hardware job openings in the United States as of June 2026, with employment types broken down into 84% Full Time, 12% Part Time, and 4% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Senior Deep Learning Kernel Software Performance Architect

Senior Deep Learning Kernel Software Performance Architect

NVIDIA

Santa Clara, CA • On-site

$196K/yr

Full-time

Posted 21 days ago

Job description

Job Summary:
NVIDIA is seeking extraordinary architects to develop processor and system architectures that accelerate machine learning, data analytics, and high-performance computing applications. The Senior Kernel Performance Architect for Deep Learning Software will craft GPU-accelerated system architectures, prototype high-performance software, and collaborate with various teams to optimize deep learning performance.
Responsibilities:
• Craft GPU-accelerated system architectures that push the boundaries of deep learning performance.
• Prototype high-performance software for deep learning and data analytics workloads.
• Analyze, visualize, and optimize software performance using analytical models, simulators, and test suites.
• Collaborate closely across NVIDIA teams such as:
• CUDA Compiler teams to identify performance issues.
• AI/ML training and inference performance teams to identify and optimize critical deep learning layers.
• hardware architecture performance teams to define expectation for emerging deep learning hardware features.
Qualifications:
Required:
• A Master's or PhD in Computer Science, Electrical Engineering or Computer Engineering, or equivalent experience.
• 5+ years of relevant industry or research experience.
• A strong foundation in machine learning and deep learning fundamentals to complement your expertise in computer architecture.
• A strong background in high performance kernel (such as CUTLASS), work experience on math library performance analysis and profiling to identify performance bottlenecks.
• Fluency in programming languages such as Python, C, C++.
• Experience and familiarity with GPU computing and parallel programming models.
• You have firsthand work experience with analytical performance modeling, profiling, and analysis.
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.
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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