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Machine Learning Engineer Intern information
See Irving, TX salary details
$24.5K - $29.9K
5% of jobs
$31.8K is the 25th percentile. Wages below this are outliers.
$29.9K - $35.4K
59% of jobs
$35.4K - $40.9K
9% of jobs
$41.3K is the 75th percentile. Wages above this are outliers.
$40.9K - $46.3K
17% of jobs
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4% of jobs
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2% of jobs
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3% of jobs
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0% of jobs
$68.1K - $73.6K
0% of jobs
$73.6K - $79K
0% of jobs
$79K - $84.5K
0% of jobs
$24.5K
$40.9K
$84.5K
How much do machine learning engineer intern jobs pay per year?
What do machine learning engineer interns do?
Machine Learning Engineer Interns are often involved in data preparation, feature engineering, model development, and performance evaluation under the guidance of senior engineers or data scientists. You may help implement and test machine learning algorithms, assist in cleaning and visualizing datasets, and contribute to code reviews or research tasks. Interns frequently collaborate with cross-functional teams, such as data scientists, software engineers, and product managers, to solve real-world problems and support ongoing projects. This hands-on experience provides valuable insights into the practical application of machine learning in a professional setting.
What is a machine learning engineer intern?
A Machine Learning Engineer Intern is a temporary, entry-level role where individuals work with data scientists and engineers to develop, test, and optimize machine learning models. Interns typically assist in data preprocessing, feature engineering, model training, and evaluation. They may also work on improving existing algorithms, implementing research papers, or deploying models into production. This role provides hands-on experience with machine learning frameworks such as TensorFlow and PyTorch, as well as coding in Python and working with large datasets. The internship helps build practical skills and industry experience in artificial intelligence and data science.
What skills and qualifications are needed to thrive as a machine learning engineer intern?
To thrive as a Machine Learning Engineer Intern, you need a solid understanding of programming languages such as Python, knowledge of machine learning algorithms, and experience with data analysis, typically supported by coursework in computer science or related fields. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and version control systems such as Git is often required. Strong problem-solving abilities, attention to detail, and effective communication are valuable soft skills in this role. These competencies enable interns to contribute meaningfully to projects, collaborate efficiently with teams, and adapt in a fast-paced, tech-driven environment.

Job description
AI Engineer Intern - AI Center of Excellence (CoE)
Location: Plano, Texas, USA
Internship Duration: 6-12 months (12 months preferred)
Company: Black Box
Eligibility: Master's students with at least 6 months remaining before graduation and prior professional experience in applied AI
Company Overview
Black Box Network Services is a leading global communications system integrator specializing in designing, sourcing, implementing, and managing complex technology solutions. As part of our strategic transformation, Black Box is expanding its AI Center of Excellence (CoE) to deliver enterprise-grade AI solutions across multiple business domains.
The AI CoE focuses on building scalable, secure, and production-ready AI systems, establishing best practices for enterprise AI adoption, and integrating AI capabilities into core business platforms.
Role Summary
As an AI Engineer Intern in the AI Center of Excellence (CoE), you will contribute to the design, development, and integration of applied AI solutions using pre-trained Large Language Models (LLMs), traditional machine learning techniques, and deterministic approaches.
This role offers hands-on experience building enterprise-grade Generative AI solutions across backend services, data pipelines, orchestration, and user-facing applications. Working closely with experienced AI engineers, you will contribute to real-world AI use cases integrated with platforms such as ServiceNow, SAP, Salesforce, and Azure services.
This internship is designed to strengthen applied AI engineering skills and prepare candidates for conversion into a full-time AI Engineer role.
Eligibility Requirements
- Currently pursuing a Master's degree in Engineering or a related field (Computer Science, Artificial Intelligence, Data Science, or similar).
- Must have at least 6 months remaining to complete the Master's program at the time of joining.
- Must have a minimum of 2 years of relevant professional experience between Bachelor's and Master's programs.
- Prior experience must include applied AI / Machine Learning, with hands-on exposure to Generative AI use cases.
- Available for a full-time, on-site internship for a minimum of 6-12 months (depending on academic program constraints).
Key Responsibilities AI & Generative AI Development
- Build and integrate AI solutions using pre-trained LLMs for conversational AI, summarization, and enterprise knowledge retrieval.
- Implement RAG-based architectures connecting LLMs with structured and unstructured enterprise data.
- Develop and test AI agents, traditional ML models, and deterministic logic for real-world use cases.
- Contribute to AI orchestration using LangChain and workflow automation using n8n.
Full-Stack & Enterprise Integration
- Build AI-enabled user interfaces and integrate them with backend services.
- Develop and maintain backend APIs and services.
- Integrate AI solutions with enterprise platforms such as ServiceNow, SAP, Salesforce, and Azure services.
Data, Testing & Deployment
- Build and maintain data pipelines, including preprocessing and quality checks.
- Support testing, debugging, deployment, and monitoring of AI services on Azure.
- Document AI workflows, integrations, and solution lifecycle updates.
Learning & Collaboration
- Collaborate with AI, data, and platform teams to deliver production-ready AI solutions.
- Continuously learn and apply best practices in Generative AI, RAG patterns, and enterprise AI systems.
Required Technical Skills
- Programming: Strong working knowledge of Python.
- Applied AI / GenAI: Hands-on experience building or integrating ML or Generative AI solutions.
- Generative AI: Practical experience with LLMs, prompt engineering, and/or RAG-based architectures.
- Backend Development: Experience building APIs using FastAPI, Flask, or Node.js (TypeScript).
- Frontend Development: Working experience building React-based user interfaces and integrating them with backend APIs.
- Data Handling: Experience working with structured and unstructured data, including basic preprocessing or ETL.
- APIs & Cloud: Experience consuming REST APIs and familiarity with cloud platforms (Azure preferred).
Required Prior Professional Experience
- 2+ years of relevant professional experience between Bachelor's and Master's programs.
- Experience in applied AI, machine learning, or software engineering with AI components.
- Ability to translate AI concepts into working prototypes or production-ready solutions.
Required Soft Skills
- Strong learning mindset, ownership, and clear communication with a structured problem-solving approach.
Preferred Skills / Experience
- Familiarity with NLP concepts and foundational Generative AI models.
- Awareness of responsible AI and basic AI governance concepts.
- Exposure to Microsoft Power Platform or low-code automation tools.
About Black Box
Black Box is a leading technology solutions provider focused on accelerating customer success through innovation, ownership, transparency, and collaboration. With over 2,500 team members across 24 countries, Black Box delivers high-value solutions globally and is a wholly-owned subsidiary of AGC Networks. Black Box is an equal opportunity employer.