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Embedded Machine Learning Internship information

What is an embedded machine learning internship?

An Embedded Machine Learning Internship is a temporary position designed for students or recent graduates to gain hands-on experience in developing and deploying machine learning algorithms on embedded systems. These internships typically involve working with hardware such as microcontrollers, sensors, or edge devices, and using specialized tools to optimize machine learning models for low-power and resource-constrained environments. Interns collaborate with engineers and data scientists to create efficient, real-world AI solutions that run directly on devices rather than relying on cloud computing. This role helps bridge the gap between theoretical machine learning concepts and practical implementation on embedded platforms.

What are some typical projects or tasks I might work on during an embedded machine learning internship?

During an Embedded Machine Learning Internship, you can expect to work on projects such as optimizing machine learning models to run efficiently on hardware with limited resources, integrating AI algorithms into embedded systems (like microcontrollers or IoT devices), and performing real-time data processing. You'll likely collaborate closely with software engineers and hardware designers to test models on physical devices, debug performance issues, and contribute to documentation. These experiences provide practical exposure to the challenges of deploying AI in real-world, resource-constrained environments and help build skills valuable for a future career in embedded AI.

What are the key skills and qualifications needed to thrive as an embedded machine learning intern, and why are they important?

To thrive as an Embedded Machine Learning Intern, you need a background in computer science, electrical engineering, or a related field with strong programming skills in C/C++ and Python, as well as foundational knowledge of machine learning algorithms. Experience with embedded systems development tools (such as ARM Cortex, Raspberry Pi, or Arduino), version control systems, and familiarity with ML frameworks like TensorFlow Lite or Edge Impulse is often required. Analytical thinking, problem-solving ability, and effective teamwork are vital soft skills for success in this role. These skills and qualities are crucial for efficiently developing, optimizing, and deploying machine learning solutions on resource-constrained embedded platforms.
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Embedded Machine Learning Internship jobs near you

Research Engineer / Robotics / ML

Motion Recruitment Partners, LLC

Lenexa, KS • On-site

Other

Posted 4 days ago

Job description

A robotics and AI team in Kansas is looking for a Research Engineer to help build the systems that power real world machine learning. This is a full-time role where you will be working across robotics, computer vision, and applied ML, using tools like ROS/ROS2, Python, C++, and PyTorch/TensorFlow.
Robots are out in the real-world collecting data, and your job is to help make sure that data is usable, structured, and feeding into machine learning models in the right way. If you are early in your career and want real exposure to robotics, ML and data systems all in one place, this is a rare setup.
Required Skills & Experience
  • 1-2 years of experience with robotics and machine learning (Internship experience acceptable)
  • Graduated with a bachelor's degree in Computer Science
  • Experience with ROS/ROS2 frameworks
  • Proficiency in Python and C++
  • Worked with ML models (PyTorch/TensorFlow/ sklearn)
  • Experience working with data pipelines or making raw data usable for AI systems
Desired Skills & Experience
  • Exposure to robotic manipulators or mobile robot systems
  • Experience with biometric/ identity verification systems
  • Familiarity with cloud platforms
What You Will Be Doing
Tech Breakdown
  • 40% Robotics Systems
  • 35% Machine Learning
  • 25% Data Engineering
Daily Responsibilities
  • 50% Hands On
  • 30% Team Collaboration
  • 20% Systems improvement

Applicants must be currently authorized to work in the US on a full-time basis now and in the future.

Apply