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Internship Machine Learning Hardware Jobs in Texas

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.

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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 popular job titles related to Internship Machine Learning Hardware jobs in Texas? For Internship Machine Learning Hardware jobs in Texas, the most frequently searched job titles are:
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What cities in Texas are hiring for Internship Machine Learning Hardware jobs? Cities in Texas with the most Internship Machine Learning Hardware job openings:
Internship Machine Learning Hardware jobs near you
Infographic showing various Internship Machine Learning Hardware job openings in Texas as of July 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 93% Physical, 3% Hybrid, and 4% Remote job distribution.
Machine Learning Engineer

Machine Learning Engineer

Neuralink

Austin, TX • On-site

$199K - $331K/yr

Full-time

Medical, Dental, Vision, Retirement

Posted 28 days ago

Job description

About Neuralink:
We are creating devices that enable a bi-directional interface with the brain. These devices allow us to restore movement to the paralyzed, restore sight to the blind, and revolutionize how humans interact with their digital world.
About the Team:
The BCI team develops the software and systems that communicate with the brain. These systems decode raw neural signals into useful actions, such as moving a cursor, typing, or actuating a robotic arm. Additionally, real-world data, such as video feeds, can be encoded into neural data to project images into the visual cortex. We also work closely with users to gather feedback, make improvements, and fundamentally reshape the user experience and interface of the BCI.
About the Role:
Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop state-of-the-art neural encoders and decoders. No prior knowledge of neuroscience is required; we value simple solutions grounded in first principles.
Neuralink designs all hardware in-house, from custom ASICs to thin-film arrays. There is no part of the technical design that cannot change. Learnings from your work will directly influence next-generation device architecture.
Job Responsibilities:
  • Telepathy Product: Develop and refine models that decode neural data, enabling individuals with paralysis to reliably type at 35 words per minute or control robotics arms for activities of daily living.
  • Blindsight Product: Formulate research questions to guide the development of neural networks and signal processing algorithms that will restore vision to those affected by blindness.
  • Utilize your fundamental understanding of neural networks and data science to develop models that serve as the foundation for machine learning applications for BCI.
  • Lead the team by performing at a high standard, setting the bar for how we build and operate our systems.
  • Inform our hardware roadmap by understanding users and identifying the product features that would have the greatest impact on their quality of life.
About You:
  • Experience writing production-level C/C++/Rust and Python
  • Proven track record of designing, building, and shipping real-time ML products
  • Strong foundation in signal processing, algorithms, and software engineering principles
  • Bachelor's degree in relevant field or equivalent experience

Fast forward to 40:32 to learn more about neural decoding:
Expected Compensation:
The anticipated base salary for this position is expected to be within the following range. Your actual base pay will be determined by your job-related skills, experience, and relevant education or training. We also believe in aligning our employees' success with the company's long-term growth. As such, in addition to base salary, Neuralink offers equity compensation (in the form of Restricted Stock Units (RSU)) for all full-time employees.
Base Salary Range:
$199,000-$331,000 USD
What We Offer:
Full-time employees are eligible for the following benefits listed below.
  • An opportunity to change the world and work with some of the smartest and most talented experts from different fields
  • Growth potential; we rapidly advance team members who have an outsized impact
  • Excellent medical, dental, and vision insurance through a PPO plan
  • Paid holidays
  • Commuter benefits
  • Meals provided
  • Equity (RSUs) *Temporary Employees & Interns excluded
  • 401(k) plan *Interns initially excluded until they work 1,000 hours
  • Parental leave *Temporary Employees & Interns excluded
  • Flexible time off *Temporary Employees & Interns excluded

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