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Internship Machine Learning Hardware Jobs in California

Brahma Consulting Group

Head of Hardware

Palo Alto, CA

$145K - $191K/yr

We are seeking an experienced Head of Hardware to lead our hardware engineering efforts at an innovative AI startup revolutionizing chip design through machine learning. This pivotal leadership role ...

Advatix Inc.

Machine Learning Engineer

San Mateo, CA · On-site

$110 - $165/hr

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift ... Work closely with founders, mechanical engineers, hardware engineers, and early customers to ...

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 ...

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Internship Machine Learning Hardware information

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 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.

What are the key skills and qualifications needed to thrive as an internship in 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 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 are the most commonly searched types of Machine Learning Hardware jobs in California?

The most popular types of Machine Learning Hardware jobs in California are:

What cities in California are hiring for Internship Machine Learning Hardware jobs?

Cities in California with the most Internship Machine Learning Hardware job openings:

Internship Machine Learning Hardware jobs near you

Machine Learning Hardware Architect - Silicon

Sunnyvale, CA

Meta
Internet and IT • 10K+ employees

7.8

Company rating: 7.8 out of 10

Based on 45 frontline employees who took The Breakroom Quiz

139th of 246 rated software companies

People enjoy working here

Good employer

Recommended by students

Learn more about Metaexternal

$212K/yr

Full-time

Re-posted 28 days ago

Job description

Meta’s mission is to give people the power to build community and bring the world closer together. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities — we're just getting started.Reality Labs (RL) focuses on delivering Meta's vision through Virtual Reality (VR) and Augmented Reality (AR). The compute performance and power efficiency requirements of Virtual and Augmented Reality require custom silicon. Reality Labs Silicon team is driving the state of the art forward with breakthrough work in computer vision, machine learning, mixed reality, graphics, displays, sensors, and new ways to map the human body. Our chips will enable AR & VR devices where our real and virtual world will mix and match throughout the day. We believe the only way to achieve our goals is to look at the entire stack, from transistors, through architecture, firmware, and algorithms. In this position you will work with Machine Learning Hardware Architects, Digital Designers, and Software engineers to develop custom Machine Learning Hardware accelerators for delivery into multiple SoCs. You will collaborate with a world-class group of researchers and architects to implement and contribute to the development and optimization of low power machine learning accelerators and state-of-the-art SoCs.
Machine Learning Hardware Architect - Silicon Responsibilities:
  • Technical lead for ML Hardware engineers, driving design from Architecture through to Product for AR/VR optimized silicon
  • Lead designs to surpass state of the art for metrics such as compute, bandwidth, and power consumption
  • Work across disciplines, brainstorm big ideas, work in new technology areas, juggle/coordinate multiple initiatives, drive a concept into a prototype and ultimately guide the transition into a high-volume consumer product
  • Travel both domestically and internationally

Minimum Qualifications:
  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 12+ years of experience as a Hardware Design Engineer or Silicon Architect for production silicon shipped in volume
  • Experience in Machine Learning IPs Silicon development
  • Experience in digital design µArchitecture, RTL coding
  • Experience with methods for partitioning a solution across hardware and software, evaluating trade-offs such as speed, performance, power, area
  • Results oriented, proactive with demonstrated creative & critical thinking

Preferred Qualifications:
  • Master/PhD degree in EE/CS or equivalent areas
  • Knowledge of Physical Design and Low power implementation
  • Experience with Firmware, DSP coding and optimization
  • Collaborate and/or lead in a team environment
  • Experience with SoC Architecture and subsystem Integration
  • Knowledge of industry trends and disruptive technologies
  • Experience in deep learning algorithms and techniques, e.g., convolutional neural networks, transformers, LLMs

About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$212,000/year to $294,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.

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