Intern, Technology, AI Platform Infrastructure
New
S
SephoraAI infrastructure
Must permanently reside and work in the U.S., Work schedule must align with 9-5PM, Pacific Standard Time; Monday - Friday, PSTInternshipEntry
Salary30 USD per hour
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Job Details
- Required Skills
- PythonBashCloud ComputingGitJavaGoCI/CDRESTful APIsLinuxNetworking
Requirements
- Currently pursuing a Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Systems, Data Science, Applied Mathematics, or a related technical field.
- Graduating in 2027 or 2028 with an undergraduate or graduate degree.
- Have foundational programming or scripting experience in Python, Java, Go, JavaScript/TypeScript, Bash, or a comparable language.
- Have a basic understanding of Linux, networking, APIs, Git, cloud computing, containers, or CI/CD concepts through coursework, projects, or prior experience.
- Be able to break down problems, learn unfamiliar technology, document work clearly, and communicate with technical and non-technical audiences.
- Have an interest in AI infrastructure, platform engineering, cloud engineering, DevOps, SRE, developer experience, or distributed systems.
- Have exposure to one or more cloud platforms, infrastructure-as-code or CI/CD tools, containers, observability concepts, secure software development, or AI/ML platform technologies.
- Be available June 14–August 20, 2027, for the 10-week internship.
- Work a schedule aligned with 9-5PM Pacific Standard Time.
Responsibilities
- Learn the AI platform architecture, infrastructure standards, security expectations, and engineering workflows.
- Partner with cloud, platform, DevOps, SRE, and AI engineering stakeholders to define a problem and measurable acceptance criteria.
- Build and test an infrastructure or automation proof of concept in a controlled non-production environment.
- Use infrastructure-as-code, version control, code review, automated testing, and secure configuration practices.
- Instrument the solution with logs, metrics, traces, or dashboards to measure health, reliability, usage, performance, or cost.
- Evaluate reliability, scalability, operational cost, supportability, failure handling, and recovery tradeoffs.
- Document findings through setup guidance, runbooks, architecture diagrams, test evidence, and a handoff package.
- Present project outcomes, evidence, lessons learned, and recommended next steps.
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