AI Intern

McAfee•Frisco, TX

1d•Hybrid

About The Position

Role Overview: Are you a curious, technically-driven student who wants to become an AI Intern and join our Data & AI Engineering team for this Summer Internship (June 01 – Aug 14)? You will contribute to the development and optimization of AI-powered data pipelines and intelligent tooling. You will work alongside experienced engineers, data scientists, and talent technology professionals to build solutions that move fast and scale further. This is a hands-on internship — you will ship real work, not just shadow meetings. Expect to contribute to production-grade systems using Databricks, AI APIs, and emerging developer tooling from day one. This is a Hybrid position located in Frisco, TX. We value in-office collaboration and meaningful interactions, so expect that you will be able to be in the office 3 days per week throughout your Internship. When you are not working on-site, you will be working from your home office. We are only considering candidates within a commutable distance to Frisco, TX, as we are not offering relocation assistance at this time. About the Role Build and iterate on AI-assisted data pipelines within the Databricks Platform, including Delta Lake transformations and MLflow experiment tracking Design, Build, and document Databricks workflows, notebooks, Genies and automated jobs — covering unit tests, integration tests, and data quality validation Leverage Claude Code to accelerate development tasks including code generation, documentation drafting, debugging assistance, and workflow automation Collaborate with the Data Engineering team to identify opportunities where AI tooling can reduce manual overhead across the SDLC Contribute to AI engineering and AI solution evaluation frameworks for internal LLM-powered tools Participate in code reviews, sprint ceremonies, and technical design discussions alongside senior engineers Document findings, tool evaluations, and process improvements in a format that enables knowledge transfer across the team Support exploratory research into emerging AI developer tools, presenting recommendations backed by hands-on prototyping About You Must be currently enrolled at an accredited college or university program for a graduate degree (MS/MA) OR undergraduate degree (BS/BA/BBA). Undergraduates must be entering their senior year to be considered Prefer students currently pursuing a degree in Computer Science, Data Engineering, Information Systems, or related technical field Must be able to commit to the full Summer Internship from June 01 to Aug 14, 2026 Hands-on experience with Spark and Big Data Proficiency in Python; SQL fluency required Familiarity with notebook-based development in Python or Scala Understanding of cluster configuration, job scheduling, and workspace organization Exposure to Databricks Unity Catalog, MLflow, or Databricks SQL is a strong plus Direct experience using Claude Code as an agentic coding assistant — including using it for code generation, test writing, refactoring, and documentation within a terminal or IDE environment Ability to read, interpret, and optimize Spark execution plans and identify performance bottlenecks in large-scale data transformations Experience writing test cases for data pipelines, including schema validation, row counts, null checks, and business rule enforcement Working knowledge of testing frameworks such as pytest, Great Expectations, or dbt tests applied within Databricks notebook environments Understanding of CI/CD principles as applied to data engineering workflows — including how automated tests plug into pipeline deployment Comfort debugging failed Databricks runs, reading logs, and isolating root cause across multi-stage pipelines Ability to craft effective prompts for Claude Code that produce accurate, context-aware outputs across multiple file types and codebases Familiarity with Claude Code’s agentic capabilities, including multi-step task execution, file editing, and bash command integration Understanding of when and how to review, modify, and validate AI-generated code rather than accepting outputs uncritically Awareness of responsible AI development practices — including model limitations, prompt injection risks, and the importance of human-in-the-loop review Curiosity about the broader Anthropic model ecosystem and an interest in staying current on emerging capabilities and APIs Exposure to cloud platforms (AWS, Azure, or GCP) and an understanding of cloud-native data architecture patterns Familiarity with version control (Git), collaborative development workflows, and pull request best practices Solid interpersonal skills; can build effective relationships with other team members Thrive in fast-paced environments and open to change in the workplace Strong team player with a positive, professional attitude Proactive problem-solver with strong tolerance for ambiguity Desire to learn from and share your knowledge with your team #LI

Requirements

Must be currently enrolled at an accredited college or university program for a graduate degree (MS/MA) OR undergraduate degree (BS/BA/BBA). Undergraduates must be entering their senior year to be considered
Must be able to commit to the full Summer Internship from June 01 to Aug 14, 2026
Hands-on experience with Spark and Big Data
Proficiency in Python; SQL fluency required
Familiarity with notebook-based development in Python or Scala
Understanding of cluster configuration, job scheduling, and workspace organization
Ability to read, interpret, and optimize Spark execution plans and identify performance bottlenecks in large-scale data transformations
Experience writing test cases for data pipelines, including schema validation, row counts, null checks, and business rule enforcement
Working knowledge of testing frameworks such as pytest, Great Expectations, or dbt tests applied within Databricks notebook environments
Understanding of CI/CD principles as applied to data engineering workflows — including how automated tests plug into pipeline deployment
Comfort debugging failed Databricks runs, reading logs, and isolating root cause across multi-stage pipelines
Ability to craft effective prompts for Claude Code that produce accurate, context-aware outputs across multiple file types and codebases
Familiarity with Claude Code’s agentic capabilities, including multi-step task execution, file editing, and bash command integration
Understanding of when and how to review, modify, and validate AI-generated code rather than accepting outputs uncritically
Awareness of responsible AI development practices — including model limitations, prompt injection risks, and the importance of human-in-the-loop review
Curiosity about the broader Anthropic model ecosystem and an interest in staying current on emerging capabilities and APIs
Exposure to cloud platforms (AWS, Azure, or GCP) and an understanding of cloud-native data architecture patterns
Familiarity with version control (Git), collaborative development workflows, and pull request best practices
Solid interpersonal skills; can build effective relationships with other team members
Thrive in fast-paced environments and open to change in the workplace
Strong team player with a positive, professional attitude
Proactive problem-solver with strong tolerance for ambiguity
Desire to learn from and share your knowledge with your team

Nice To Haves

Prefer students currently pursuing a degree in Computer Science, Data Engineering, Information Systems, or related technical field
Exposure to Databricks Unity Catalog, MLflow, or Databricks SQL is a strong plus
Direct experience using Claude Code as an agentic coding assistant — including using it for code generation, test writing, refactoring, and documentation within a terminal or IDE environment

Responsibilities

Build and iterate on AI-assisted data pipelines within the Databricks Platform, including Delta Lake transformations and MLflow experiment tracking
Design, Build, and document Databricks workflows, notebooks, Genies and automated jobs — covering unit tests, integration tests, and data quality validation
Leverage Claude Code to accelerate development tasks including code generation, documentation drafting, debugging assistance, and workflow automation
Collaborate with the Data Engineering team to identify opportunities where AI tooling can reduce manual overhead across the SDLC
Contribute to AI engineering and AI solution evaluation frameworks for internal LLM-powered tools
Participate in code reviews, sprint ceremonies, and technical design discussions alongside senior engineers
Document findings, tool evaluations, and process improvements in a format that enables knowledge transfer across the team
Support exploratory research into emerging AI developer tools, presenting recommendations backed by hands-on prototyping