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Machine Learning Remote Internship Jobs in Claymont, DE

Soni Resources

Data Scientist

Conshohocken, PA · On-site +1

$175K/yr

Remote (Preference for Northeast/Mid-Atlantic; monthly travel to Plymouth Meeting, PA as needed ... Develop predictive models, scoring frameworks, and machine learning solutions that enhance business ...

TSMG

AI/ML Data Contributor

Philadelphia, PA · Remote

... machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format, offering both remote and in-person opportunities (such as device ...

TSMG

AI/ML Data Contributor

Philadelphia, PA · Remote

... machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format, offering both remote and in-person opportunities (such as device ...

IQVIA, Inc.

AI Solutions Delivery 3 (Remote)

Wayne, PA · On-site +1

Our team offerings leverage advanced analytics, machine learning algorithms, and technology platforms for a variety of healthcare applications including finding undiagnosed patients with rare ...

IQVIA

AI Solutions Delivery 3 (Remote)

Wayne, PA · Remote

Our team offerings leverage advanced analytics, machine learning algorithms, and technology platforms for a variety of healthcare applications including finding undiagnosed patients with rare ...

IQVIA

AI Solutions Delivery 3 (Remote)

Wayne, PA · Remote

Our team offerings leverage advanced analytics, machine learning algorithms, and technology platforms for a variety of healthcare applications including finding undiagnosed patients with rare ...

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All JobsMachine Learning JobsMachine Learning Remote Internship JobsMachine Learning Remote Internship Jobs in Claymont, DE

Machine Learning Remote Internship information

See Claymont, DE salary details

$24.9K

$41.5K

$85.9K

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

As of Aug 15, 2026, the average yearly pay for machine learning remote internship in Claymont, DE is $41,543.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,700.00 and $44,900.00 per year, depending on experience, location, and employer.

What types of projects can I expect to work on during a machine learning remote internship?

During a remote machine learning internship, you can expect to contribute to projects such as data preprocessing, model development, and performance evaluation. Interns often work on real-world datasets, applying techniques like regression, classification, clustering, or deep learning, depending on the organization's focus. Collaboration with data scientists, engineers, and other interns is common, typically via virtual meetings and shared code repositories. These projects provide hands-on experience and often culminate in presenting your findings to the team, offering valuable exposure to industry-standard workflows and tools.

What is a machine learning remote internship?

A Machine Learning Remote Internship is a temporary, structured work experience where interns contribute to machine learning projects from a remote location, such as their home. Interns typically work with teams on tasks like data preprocessing, building models, and evaluating results, while gaining practical knowledge and mentoring. These internships are ideal for students or recent graduates looking to develop their skills in machine learning, programming, and data science without the need to relocate. They often involve working with Python, popular ML libraries, and real-world datasets. Communication and collaboration are maintained through online tools and regular meetings.

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

To thrive as a Machine Learning Remote Intern, you need a solid background in programming (especially Python), mathematics/statistics, and a foundational understanding of machine learning concepts, often gained through coursework or relevant projects. Familiarity with machine learning libraries (like TensorFlow, PyTorch, and scikit-learn), version control systems (such as Git), and cloud platforms is typically expected. Strong problem-solving abilities, self-motivation, and effective remote communication set top interns apart. These skills and qualities enable efficient collaboration, successful project delivery, and continuous learning in a dynamic, distributed work environment.

What is the difference between Machine Learning Remote Internship vs Data Science Intern?

AspectMachine Learning Remote InternshipData Science Intern
Required CredentialsBasic programming, math, and machine learning knowledgeStatistics, programming, and data analysis skills
Work EnvironmentRemote, collaborative teams, project-basedRemote or on-site, data analysis and modeling tasks
Industry UsageTech, AI, startups, research labsTech, finance, healthcare, consulting
Search & Comparison IntentUnderstanding internship roles in MLExploring data science internship opportunities

Machine Learning Remote Internships focus on developing models and algorithms, often requiring knowledge of programming and math. Data Science Internships involve analyzing data, creating reports, and supporting decision-making. While both roles are remote and industry-relevant, ML internships emphasize algorithm development, whereas data science roles focus on data analysis and visualization.

What are popular job titles related to Machine Learning Remote Internship jobs in Claymont, DE?

For Machine Learning Remote Internship jobs in Claymont, DE, the most frequently searched job titles are:

What job categories do people searching Machine Learning Remote Internship jobs in Claymont, DE look for?

The top searched job categories for Machine Learning Remote Internship jobs in Claymont, DE are:

Machine Learning Remote Internship jobs near you

Engineer 6, Machine Learning, Data & AI

Comcast

Philadelphia, PA • On-site, Remote

$115K - $138K/yr

Full-time

Posted 3 days ago

New

Job description

Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You'll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.)

Job Summary

We are seeking a visionary Senior Technologist, Data & Agentic AI Enablement to shape the future of our enterprise data ecosystem and accelerate the next generation of AI-driven experiences. This enterprise leadership role is responsible for two complementary missions:
Preparing enterprise data for an Agentic AI future, ensuring data is trusted, discoverable, semantically rich, governed, and consumable by AI agents. Transforming data engineering through Agentic AI, embedding AI across the data engineering lifecycle to improve productivity, quality, reliability, and speed of delivery. The successful candidate will define the architecture, standards, and engineering patterns that enable AI to become an active participant in the design, development, testing, operation, and optimization of enterprise data platforms. This highly influential role partners closely with Data Engineering, Data Platforms, Enterprise Architecture, Security, Product, and AI teams to accelerate data modernization and AI-enabled business transformation.

Job Description

Agentic AI Transformation of Data Engineering:

Lead the strategy for integrating Agentic AI across the enterprise data engineering lifecycle, enabling AI-assisted development, operations, and platform management.

Responsibilities include:

  • Define the enterprise roadmap for incorporating AI agents into data engineering workflows, platform operations, and software delivery.
  • Partner with platform engineering teams to integrate AI capabilities into CI/CD, Infrastructure-as-Code (IaC), and DevSecOps practices.
  • Evaluate emerging agent frameworks, copilots, and autonomous engineering platforms for enterprise adoption.
  • Establish best practices and governance for AI-assisted engineering and operational processes.
Enterprise Data Strategy for AI & Agentic Systems:

Develop and drive the enterprise strategy for preparing data assets to support AI, Generative AI, and Agentic AI use cases.

Responsibilities include:

  • Define principles and reference architectures that enable AI agents to discover, access, understand, and act upon enterprise data safely and effectively.
  • Partner with business and technology leaders to identify high-value opportunities for Agentic AI solutions.
  • Establish enterprise standards that align data, AI, and business strategies.
Data Architecture & AI Readiness

Lead architectural efforts to ensure enterprise data is optimized for both human and AI consumption.

Responsibilities include:

  • Establish standards that ensure data is:
    • Discoverable
    • Well-described and semantically rich
    • Governed and trusted
    • Accessible through standardized interfaces
    • Consumable by both people and AI agents
  • Drive adoption of metadata-driven architectures, semantic models, knowledge graphs, and business ontologies.
  • Ensure enterprise data products support machine-to-machine interactions in addition to traditional analytics use cases.
Agentic Data Enablement:

Define the frameworks that enable AI agents to effectively interact with enterprise data and knowledge assets.

Responsibilities include:

  • Establish standards for exposing enterprise data through APIs, semantic layers, data products, and retrieval systems.
  • Partner with platform teams to develop capabilities supporting:
    • Retrieval-Augmented Generation (RAG)
    • Agent orchestration platforms
    • Tool and API discovery
    • Vector-based retrieval architectures
    • Context management and memory frameworks
  • Develop patterns that allow AI agents to access enterprise knowledge securely and responsibly.
Data Governance & Trust:

Ensure governance and trust frameworks evolve to support autonomous and AI-assisted decision making.

Responsibilities include:

  • Establish controls for data lineage, provenance, quality, explainability, and auditability.
  • Partner with Security, Privacy, and Risk teams to implement responsible AI controls and secure data access practices.
  • Define trust frameworks that enable AI agents to operate within approved business guardrails.
Semantic Layer & Knowledge Management:

Drive the development of enterprise semantic capabilities that improve data accessibility and AI reasoning.

Responsibilities include:

  • Lead development of enterprise semantic models and shared business definitions.
  • Improve metadata quality, business context, and knowledge accessibility across the organization.
  • Advance enterprise knowledge management practices that enhance AI reasoning, discovery, and decision support.
Platform & Ecosystem Alignment:

Collaborate across teams to ensure enterprise platforms support AI-native consumption patterns.

Responsibilities include:

  • Partner with Data Platform, Engineering, Analytics, and Product teams to align technology roadmaps.
  • Collaborate with BI and analytics teams to maintain consistent business metrics and semantic definitions across human and AI consumers.
  • Influence technology investments that enable future AI and Agentic AI capabilities.
Innovation & Thought Leadership

Serve as a strategic thought leader on AI readiness, data modernization, and enterprise architecture.

Responsibilities include:

  • Monitor emerging trends in AI, Agentic Systems, Data Architecture, and Knowledge Management.
  • Evaluate innovative technologies and identify strategic adoption opportunities.
  • Advise executive leadership on enterprise AI strategy and future-state architecture.
Qualifications
  • + years of experience in Data Architecture, Data Engineering, Enterprise Architecture, or related technology disciplines.
  • Proven experience designing and scaling enterprise data ecosystems.
  • Experience supporting AI, machine learning, advanced analytics, or data modernization initiatives.
  • Demonstrated success influencing outcomes across large, matrixed organizations.
Technical Expertise
  • Deep understanding of modern data architectures, including data warehouses, data lakes, lakehouses, and data mesh concepts.
  • Expertise in metadata management, semantic modeling, data governance, and data product design.
  • Strong understanding of APIs, event-driven architectures, and interoperability standards.
  • Experience with AI technologies, including LLMs, RAG architectures, vector databases, agent frameworks, and AI orchestration platforms.
  • Knowledge of modern software engineering, DevSecOps, CI/CD, and cloud-native architectures.
Leadership & Communication
  • Strong executive communication and stakeholder management skills.
  • Ability to translate emerging technologies into practical enterprise strategies.
  • Proven ability to lead through influence across business and technology organizations.
  • Demonstrated thought leadership in data, AI, or enterprise architecture domains.
Preferred Candidate Profile :

A strategic technology leader with deep expertise in enterprise data architecture and a passion for advancing AI adoption. This individual combines strong technical vision, architectural leadership, and business acumen to help the organization build a trusted, AI-ready data foundation while transforming the way engineering teams work through Agentic AI.

Disclaimer:

This information has been designed to indicate the general nature and level of work performed by employees in this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications.

Skills

Agentic AI, AI Adoption, Data Architecture Development, Data Engineering, Data Strategies, Enterprise Data

Compensation

This job can be performed in Virginia, and District of Columbia with a Pay Range of $224,190.44 - $351,571.37Comcast intends to offer the selected candidate base pay within this range, dependent on job-related, non-discriminatory factors such as experience. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.

Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non-sales positions are eligible for a Bonus. Additionally, Comcast provides best-in-class Benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That's why we provide an array of options, expert guidance and always-on tools, that are personalized to meet the needs of your reality - to help support you physically, financially and emotionally through the big milestones and in your everyday life. Please visit the compensation and benefits summary on our careers site for more details.

Education

Bachelor's DegreeWhile possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience.

Certifications (if applicable)

Relevant Work Experience

15 Years +Comcast is an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law.

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