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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 ...

Qualcomm

Sr. Staff, Engineer

San Diego, CA · On-site

$110K - $152K/yr

Qualcomm Engineers collaborate with cross-functional teams to enhance the world of mobile, edge, auto, and IOT products through machine learning hardware and software. Minimum Qualifications: • ...

Qualcomm

Staff Engineer

San Diego, CA · On-site

Qualcomm Engineers collaborate with cross-functional teams to enhance the world of mobile, edge, auto, and IOT products through machine learning hardware and software. Minimum Qualifications: • ...

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 ...

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 ...

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

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.
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 are popular job titles related to Internship Machine Learning Hardware jobs in California? For Internship Machine Learning Hardware jobs in California, the most frequently searched job titles are:
What job categories do people searching Internship Machine Learning Hardware jobs in California look for? The top searched job categories for Internship Machine Learning Hardware jobs in California are:
Internship Machine Learning Hardware jobs near you

Head of Hardware

Brahma Consulting Group

Palo Alto, CA

$145K - $191K/yr

Full-time

Posted 9 days ago

Job description


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 is ideal for a seasoned professional with a passion for driving complex hardware projects, mentoring teams, and shaping the future of AI-driven chip design in a fast-paced, cutting-edge environment.


Key Responsibilities

  • Lead Hardware Engineering Strategy: Oversee all hardware engineering efforts, setting the vision and strategy for RTL development and chip design, while serving as the primary technical authority for ML engineers learning hardware design.
  • Establish Validation Frameworks: Develop and implement evaluations, benchmarks, and measurement frameworks to validate ML-generated hardware designs for accuracy, performance, and scalability.
  • Collaborate with ML Team: Partner closely with the ML team to translate hardware requirements and industry best practices into actionable guidance, ensuring ML models align with chip design goals.
  • Mentor and Build Teams: Guide and mentor founding hardware engineers, fostering a culture of excellence and establishing robust hardware design methodologies and processes.
  • Drive Client Engagement: Lead client interactions to understand their chip design requirements, translating them into actionable technical specifications for the engineering team.
  • Spearhead Design Transition: Oversee the transition from tool development to production-ready chip design as the company scales, ensuring high-quality deliverables and operational efficiency.


Day-to-Day Example

Your week might involve reviewing ML-generated RTL code and providing strategic feedback to the ML team to enhance model performance. You’ll lead client discussions to align on chip design requirements, ensuring project goals meet customer needs. Additionally, you’ll mentor junior hardware engineers, sharing expertise on best practices and guiding their professional growth. For instance, last month, our team improved our ML model’s ability to generate complex digital blocks, and you would have driven the evaluation methodology, provided technical validation, and aligned the team’s efforts with company objectives. This role thrives in a dynamic, innovative environment at the forefront of AI and hardware integration.


Qualifications

  • Experience: 5+ years in hardware design and verification, with a proven track record of leading RTL development teams and delivering successful tapeouts.
  • Technical Expertise: Deep expertise in RTL design (Verilog/SystemVerilog), synthesis, and verification methodologies (UVM preferred), with a strong understanding of chip design lifecycle.
  • Leadership: Demonstrated ability to lead complex chip design projects, manage cross-functional teams, and drive technical strategy in a fast-paced environment.
  • AI Startup Fit: Comfortable thriving in a dynamic AI startup, with the ability to adapt to evolving priorities, tight timelines, and innovative challenges.


Bonus Skills:

  • Experience with machine learning concepts and their application to hardware design.
  • Strong Python programming skills for scripting, automation, and tool development.
  • Communication: Exceptional leadership and communication skills to align cross-functional teams, engage with clients, and mentor engineers.


Preferred Qualifications

  • Familiarity with AI hardware architectures (e.g., TPUs, GPUs) and their design/verification challenges.
  • Expertise with EDA tools (e.g., Synopsys, Cadence) and formal verification techniques.
  • Experience collaborating with ML teams or working with MLOps practices for model validation.