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Embedded Machine Learning Internship Jobs in Richmond, VA

As an engineer on this team, you will dive into cutting-edge machine learning feature engineering ... At least 6 years of professional software engineering experience (Internship experience does not ...

Lead a team of developers with deep experience in machine learning, distributed microservices, and ... At least 6 years of experience in application development (Internship experience does not apply)

Lead Data Engineer (Python, AWS)

Richmond, VA · On-site

$113K - $136K/yr

Work with a team of developers with deep experience in machine learning, distributed microservices ... At least 4 years of experience in application development (Internship experience does not apply)

Lead a team of developers with deep experience in machine learning, distributed microservices, and ... At least 6 years of experience in application development (Internship experience does not apply)

Lead Data Engineer

Richmond, VA · On-site

$113K - $136K/yr

Work with a team of developers with deep experience in machine learning, distributed microservices ... At least 4 years of experience in application development (Internship experience does not apply)

Showing results 21-40

Embedded Machine Learning Internship information

See Richmond, VA salary details

$25.2K

$42.1K

$87.1K

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

As of Aug 11, 2026, the average yearly pay for embedded machine learning internship in Richmond, VA is $42,142.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,200.00 and $45,500.00 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.
What are popular job titles related to Embedded Machine Learning Internship jobs in Richmond, VA? For Embedded Machine Learning Internship jobs in Richmond, VA, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Internship jobs in Richmond, VA look for? The top searched job categories for Embedded Machine Learning Internship jobs in Richmond, VA are:
What cities near Richmond, VA are hiring for Embedded Machine Learning Internship jobs? Cities near Richmond, VA with the most Embedded Machine Learning Internship job openings:

Lead Software Engineer (AWS, Golang, NodeJS)

Capital One

Richmond, VA • On-site

Full-time

Posted 27 days ago


Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

93rd of 171 rated banks


Job description

Lead Software Engineer (AWS, Golang, NodeJS)

At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine exceptional products for our customers.
In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities.
In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value.
Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors, who solve real problems and meet real customer needs. We are seeking Full Stack Software Engineers who are passionate about marrying data with emerging technologies. As a Capital One Lead Software Engineer, you'll have the opportunity to be on the forefront of driving a major transformation within Capital One.
What You'll Do:

  • Lead a portfolio of diverse technology projects and a team of developers with deep experience in distributed microservices, and full stack systems to create solutions that help meet regulatory needs for the company

  • Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, mentoring other members of the engineering community

  • Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment

  • Utilize programming languages like JavaScript, Java, HTML/CSS, TypeScript, SQL, Python, and Go, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services


Basic Qualifications:

  • Bachelor's Degree

  • At least 4 years of experience in software engineering (Internship experience does not apply)

  • At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud)


Preferred Qualifications:

  • Master's Degree

  • 7+ years of experience in at least one of the following: JavaScript, Java, TypeScript, SQL, Python, or Go

  • 3+ years of experience with AWS, GCP, Microsoft Azure, or another cloud service

  • 4+ years of experience in open source frameworks

  • 1+ years of people management experience

  • 2+ years of experience in Agile practices

  • Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion


At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

Cambridge, MA: $197,300 - $225,100 for Lead Software Engineer


McLean, VA: $197,300 - $225,100 for Lead Software Engineer


Richmond, VA: $179,400 - $204,700 for Lead Software Engineer









Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.

This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).


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