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Embedded Machine Learning Internship Jobs in Sunnyvale, CA

Velo3D

Machine Learning Engineer

Fremont, CA · On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Work with print software and embedded teams to integrate validated models into production code ...

Bland

Machine Learning Intern

San Francisco, CA · On-site

Machine Learning Research Intern, Audio As a Research Intern at Bland, you will own a focused ... We scope internships around a single meaningful question that can be answered in the time you have.

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

What we're looking for * 4+ years of non-internship professional MLE experience. * Deep expertise ... Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. * Deep ...

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Embedded Machine Learning Internship information

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

As of Sep 1, 2026, the average yearly pay for embedded machine learning internship in Sunnyvale, CA is $49,978.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,100.00 and $54,000.00 per year, depending on experience, location, and employer.

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
Infographic showing various Embedded Machine Learning Internship job openings in Sunnyvale, CA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution, with an average salary of $49,978 per year, or $24 per hour.

Mid-Level Machine Learning Engineer

TetraMem - Accelerate The World

San Jose, CA • On-site

Full-time

Re-posted 16 days ago

Job description

Job Summary:
TetraMem is a company focused on accelerating the world through innovative technology, and they are seeking a Mid-Level Machine Learning Engineer to develop and optimize machine learning models for edge AI applications. The role involves collaborating with hardware and software teams, providing mentorship, and researching state-of-the-art ML techniques to enhance model efficiency.
Responsibilities:
• Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.
• Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.
• Work closely with hardware and software teams to integrate ML models into production systems.
• Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.
• Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.
• Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.
• Provide technical leadership and mentorship to junior engineers.
• Publish research findings, present at conferences, and contribute to open-source projects when applicable.
Qualifications:
Required:
• 5+ years of experience or PhD in Computer Science, Electrical Engineering, or related fields.
• Strong experience in machine learning, with a focus on edge AI and lightweight model deployment.
• Expertise in ML frameworks such as PyTorch, TensorFlow, JAX.
• Proficiency in programming languages such as C/C++, Python, and experience with ML model optimization.
• Ability to work independently and collaboratively in a fast-paced startup environment.
• Ability to provide mentorship, technical guidance, and career development support to junior engineers and interns.
Preferred:
• Understanding of ML compiler and runtime design.
• Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML.
• Familiarity with hardware acceleration techniques.
• Experience in embedded system development.
Company:
TetraMem is developing cutting-edge analog computing solutions for AI applications, offering exceptional performance with ultra-low power consumption. Founded in 2018, the company is headquartered in Newark, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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