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Embedded Machine Learning Internship Jobs in Massachusetts

Capital One

Lead Machine Learning Engineer

Cambridge, MA · On-site +1

$112K - $147K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Cognex

Senior Machine Learning Engineer

Natick, MA

$115K - $225K/yr

Cognex is a global leader in the exciting and growing field of machine vision. Our employees ... The team works across custom hardware, optimized embedded systems, and nextgeneration algorithm ...

Motional

Senior Embedded Software Engineer

Boston, MA · On-site +1

$134K - $176K/yr

... machine learning, sensors, and hardware compute platforms to evolve Motional's next-generation on ... If you are a software engineer and love the idea of working on embedded AI hardware and software ...

Amazon

Machine Learning Engineer, Alexa AI

Boston, MA · On-site

The Alexa AI team is looking for a passionate, talented, and inventive Machine Learning Engineer ... BASIC QUALIFICATIONS - 3+ years of non-internship professional software development experience - 2+ ...

Capital One

Sr. Lead Machine Learning Engineer

Cambridge, MA · On-site +1

$112K - $147K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Xometry

Staff Machine Learning Engineer

Waltham, MA · On-site

... Xometry's embedded DFM AI + IQE integration with Teamcenter and Designcenter. You will be ... machine learning engineering, with a track record of owning and delivering complex ML systems in ...

Xometry

Staff Machine Learning Engineer

Waltham, MA · On-site

... Xometry's embedded DFM AI + IQE integration with Teamcenter and Designcenter. You will be ... machine learning engineering, with a track record of owning and delivering complex ML systems in ...

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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.
What are the most commonly searched types of Embedded Machine Learning jobs in Massachusetts? The most popular types of Embedded Machine Learning jobs in Massachusetts are:
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Embedded Machine Learning Internship jobs near you
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Cognex Corporation

Natick, MA • On-site

$115K - $225K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 8 days ago

Job description

Job Description
About Us:
Cognex is a global leader in the exciting and growing field of machine vision. Our employees, proudly called "Cognoids," are passionate about solving the most difficult vision problems with our embedded cameras and software products featuring state-of-the-art ID, 2D and 3D vision technology. Our Work Hard, Play Hard, Move Fast culture recognizes achievement and dedication with unique rewards and celebrations.
We are looking for creative, bright, motivated Cognoids who share our passion for excellence and want to make an impact at a dynamic, global company. We celebrate our employees for their innovation, perseverance, and hard work in a fun, rewarding, and quirky environment. If you enjoy the sense of accomplishment that comes from working together to create products that solve tough problems for organizations around the world, contact us to see how you can become part of our team!
This is a hybrid role based in our Natick, MA headquarters.
The Team
You will join the Core Vision Tools team that is responsible for building state-of-the-art computer vision algorithms and deep learning models used in all Cognex's products. The team works across custom hardware, optimized embedded systems, and next-generation algorithm design to deliver high-performance solutions that run at extreme speeds and scale globally across product lines.
Job Summary
We are seeking an experienced AI/ML engineer with strong research and development skills-someone deeply familiar with designing, training, evaluating, and deploying efficient deep learning models for computer vision. You will advance Cognex's AI capabilities by developing novel architectures, optimizing models for embedded platforms, and collaborating with R&D and product teams to bring research prototypes into production.
Essential Functions
  • Research, design, and implement efficient deep learning models for industrial machine vision tasks, with a focus on algorithms with low power, low latency and data efficiency requirements

  • Collaborate with cross functional engineers to transition experimental AI models into production-ready components for embedded systems.

  • Develop high-performance Python and C/C++ code for training, optimization, benchmarking, and deployment.

  • Lead model-architecture discussions and make long-term technical decisions across platforms.

  • Optimize neural networks for resource-constrained environments (quantization, pruning, distillation, hardware-aware design).

  • Build evaluation pipelines, datasets, and tools to assess model accuracy, robustness, runtime performance, and reliability.

  • Diagnose and resolve complex issues across hardware, software, and ML components.

  • Provide technical guidance to engineers developing UIs, test frameworks, and runtime components.

  • Mentor junior engineers and champion engineering excellence in ML research and development.

Knowledge, Skills, and Abilities
Essential
  • Industry or academic experience developing and optimizing deep learning algorithms in one or more relevant technical areas - computer vision, natural language processing, speech recognition

  • Deep understanding of AI concepts including training strategies, loss functions, evaluation metrics, and ML operations.

  • Strong Python programming skills.

  • Proficient C/C++ experience for performance-critical systems.

  • Proficiency with ML frameworks (PyTorch, TensorFlow), model optimization, and ML development lifecycles.

  • Strong debugging and analytical problem-solving skills.

  • Experience with software development practices including version control, CI/CD, and issue tracking.

  • Excellent communication and collaboration skills.

Desired
  • Background in computer vision, signal processing, or related fields.

  • Experience with embedded ML, quantization, or hardware-aware optimization.

  • Hands-on experience building and deploying efficient deep learning models for real-world computer vision applications.

Minimum Education & Experience
Bachelor's or Master's degree in Computer Science, Computer/Electrical Engineering, Mathematics or related field, and 5+ years of relevant experience in AI/ML, software engineering, or applied research roles.
Cognex believes in fair and equitable pay. A reasonable estimate of the base salary range for this role is $115,000 USD - $225,000 USD. Please note that actual salaries may vary within the range, or be above or below the range, based on factors including, but not limited to, education, training, experience, professional achievement, business need, and location. In addition to base salary, employees will participate in either an annual bonus plan based on company and individual performance, or a sales incentive plan.
This position provides a comprehensive benefits package, including health, dental, and vision insurance; a 401(k) retirement plan with company matching; employer-paid disability, family leave, and life insurance; paid time off (including holidays); optional voluntary benefits; as well as recognition and wellness programs.
Additional Job Description
Equal Employment Opportunity
Cognex is an equal opportunity employer. Cognex evaluates qualified applicants without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, protected veteran status, disability/handicap status or any other legally protected characteristic.

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