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Embedded Machine Learning Internship Jobs in Sunnyvale, CA

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 ...

Atoms

Staff Machine Learning Engineer

San Francisco, CA · On-site

What we're looking for * 10+ years of non-internship professional MLE experience. * Deep expertise ... Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. * Deep ...

Atoms

Staff Machine Learning Engineer

San Francisco, CA · On-site

What we're looking for * 10+ years of non-internship professional MLE experience. * Deep expertise ... Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. * Deep ...

Veryfi

Sr. Machine Learning Engineer

San Mateo, CA · On-site

$139K - $184K/yr

... internship Preferred : • Preferred degree in Computer Science, Mathematics or similar courses or fields • A demonstrated experience of working in Machine Learning Jobs before is an added ...

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All JobsEmbedded Machine Learning JobsEmbedded Machine Learning Internship JobsEmbedded Machine Learning Internship Jobs in Sunnyvale, CA

Embedded Machine Learning Internship information

See Sunnyvale, CA salary details

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$103.3K

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

As of Sep 2, 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.

Software Engineer - Machine Learning

Quantiply Corporation

San Jose, CA • On-site

Full-time

Re-posted 16 days ago

Job description

Company Description

Did you know that Money Laundering is a primary enabler of criminal activity like drug trafficking, smuggling, terrorism, and corruption around the world with an estimated $2.3 Trillion laundered annually? Criminals are becoming more and more sophisticated in rapidly innovating new ways to launder money and current methods of detecting money laundering are antiquated and ineffective. Quantiply's Sensemaker application suite and platform solutions use AI and machine learning algorithms to identify money laundering and other criminal activities and automatically recommend mitigation strategies and actions. If you are looking for an opportunity to work on complex business problems using the latest AI and Machine Learning technologies, work with best and brightest in crafting innovative solutions while making a positive impact on society, Quantiply is the place for you.

Job Description

We're looking for software engineers with experience in machine learning and artificial intelligence. You will be embedded as part of a team that collaborates with researchers on conceiving, researching, and prototyping new machine learning techniques and use cases with the goal of driving Quantiply's growth in the Anti-Money Laundering space.

Ideal candidates will have a good understanding of state-of-the-art techniques in machine learning and deep learning, performance optimization, and benchmarking, along with a strong understanding of high-performance computer architecture. Candidates must also possess strong verbal and written communication skills and the demonstrated ability to work in a demanding team-oriented environment.

Responsibilities:

  • Develop highly scalable deep learning, reinforcement learning and bayesian models.
  • Support research projects by providing innovative designs for end-to-end Machine Learning systems.
  • Optimize performance of complex machine learning systems. Exploit modern parallel environments.
  • Design and develop software libraries.
  • Partner with Product and Engineering teams to explore new opportunities.
  • Influence product features and product roadmap through exploratory analysis.
  • Report and present software developments verbally and in writing.
Qualifications

Minimum qualifications:

  • PhD in computer science, machine learning, electrical engineering, mathematics, or equivalent pracitical experience.
  • Strong knowledge and experience in Python, Docker and Kubernetes.
  • Working experience in Tensorflow.
  • Working experience with distributed software architecture.
  • Knowledge of machine learning and statistics.

Preferred qualifications:

  • Strong experience in Tensorflow or similar frameworks.
  • Strong experience with concurrent and distributed software architecture.
  • Experience with Hadoop.
  • Experience with C++/Java/Scala.
Additional Information

All your information will be kept confidential according to EEO guidelines.

Apply