| Job Title | AI Applications Engineering Intern Position |
| Appointment Type | Student Employee |
| Job Location | Corvallis |
| Position Appointment Percent | 100 |
| Appointment Basis | 12 |
| Pay Method | Hourly |
| Pay Period | 16th - 15th of the following month |
| Pay Date | Last working day of the month |
| Remote or Hybrid option? | |
| Min Hourly Rate | $15.05 (Standard); $14.05 (Non-Urban); $16.30 (Portland Metro) |
| Max Hourly Rate | $21.50 (Standard); $21.00 (Non-Urban); $22.00 (Portland Metro) |
| Position Summary | This recruitment will be used to fill four part-time (a maximum of 24 hours per week during academic terms and a maximum of 40 hours per week during academic breaks)Student Information Technology for the Office of VP For Research at Oregon State University (OSU).
The AI Applications Engineering Intern Position offers a unique opportunity to gain hands-on experience with applied AI technologies in a collaborative and innovative setting. Interns will participate in the Platform Competition Summer Sprint (PCSS) – a GENESIS initiative for Summer 2025 in the OSU Division of Research and Innovation (DRI) – focused exclusively on building intelligent applications leveraging Multi-Agent Orchestration (MAO) targeting the following cloud-based platforms: Microsoft Azure, Google Vertex AI, or AWS Bedrock. Successful candidates will join one of the project teams building an agentic system called the “Academic Manuscript IP Evaluator” (AMIE). Through this experience, interns will deepen their technical skills in the use of generative AI “agents” in specific applications. They will build domain expertise in real-world innovation workflows, and become proficient in modern AI tools used in industry. This position will report to the Director of AI Administrative Strategy and Implementation within the DRI. |
| Position Duties | - Work in teams to design, implement, and deploy a functioning AI-enabled service (AMIE).
- Develop backend agent workflows and frontend user interfaces.
- Leverage generative AI toolchains available on major AI platforms.
- Participate in architecture design reviews, product testing, and team demos.
- Engage in weekly design scrums and maintain development documentation.
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| Minimum Qualifications | - Must be academically enrolled in a high school, community college, or university and pursuing a program or course of study
- Must meet Academic Standing Requirements; students on academic suspension are not eligible for employment
- Must meet the applicable minimal enrollment standard
- High School student: Regularly enrolled in a high school or participating in a home-schooling program
- Undergraduate and post-baccalaureate student: 6 credit hours per term
- Undergraduate international student: 12 credit hours per term*
- Graduate student officially admitted to Graduate School: 5 credit hours per term
- Graduate international student officially admitted to Graduate School: 9 credit hours per term*
*International students may be allowed to carry fewer hours than specified above and still be considered “full-time” by the United States Citizenship and Immigration Services ( USCIS). A reduced course load is approved by the Office of International Services ( OIS), and must be provided to the Student Employment Center. |
| Additional Required Qualifications | - Enrolled in a Computer Science, Data Science, Engineering, or related major at Oregon State University
- Proficient in Python, including scripting, API interaction (REST/JSON), and basic data processing
- Comfortable using Git and GitHub for collaborative software development
- Foundational knowledge of large language models (LLMs) and prompt engineering concepts
- Experience with at least one major cloud platform:
- Google Cloud Platform (Vertex AI, Gemini Studio, Cloud Functions)
- Microsoft Azure (AI Studio, Prompt Flow, Cognitive Search)
- Amazon Web Services (AWS) (Bedrock, Lambda, SageMaker)
- Basic understanding of deploying applications in cloud or containerized environments (e.g., Docker)
- Strong communication skills and a collaborative, problem-solving mindset
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| Preferred (Special) Qualifications | The following are not strict requirements, but skills and experiences that would be helpful for the project. We encourage students with curiosity and a willingness to learn to apply, even if they don’t check every box. - Familiarity with LLM development tools such as the OpenAI Assistants API, LangChain (for agent workflows and RAG pipelines), Gemini Studio on Google Vertex AI (for long-context prompt orchestration), Azure AI Studio and Prompt Flow (for visual LLM pipeline design), or Amazon Bedrock (for managed access to models like Claude, Cohere, and Mistral)
- Experience with or interest in equivalent platforms, including Azure OpenAI Services, AWS SageMaker, or custom LLM toolchains built using open-source or cloud-native frameworks
- Awareness of cloud resource usage, including token limits, compute cost estimation, and API quota management
- Familiarity with concepts in retrieval-augmented generation (RAG), semantic document search, or long-context reasoning
- Experience building and deploying containerized applications using Docker, including writing optimized Dockerfiles, managing services with Docker Compose, and integrating with cloud-native environments (e.g., Cloud Run, Azure Container Apps, AWS ECS)
- Experience developing interactive user interfaces using component-based frontend frameworks (e.g., React, Streamlit), and a strong understanding of core web technologies (HTML5, CSS3, JavaScript ES6+)
- Experience deploying full-stack or frontend applications using modern cloud hosting and CI/CD pipelines
- Understanding of academic research workflows, intellectual property (IP) evaluation, or university tech transfer processes
- Familiarity with cloud-based automation and orchestration tools (e.g., job schedulers, serverless functions, or pipeline orchestration frameworks)
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| Working Conditions / Work Schedule | - This role will be compensated for up to 40 hours per week of work during the summer and breaks and up to 24 hours per week during the school term.
- Gain applied experience with AI application technologies.
- Collaborate in a dynamic, purpose-driven team environment.
- Receive personalized mentorship and coaching from senior leaders.
- Bolster your resume with practical AI systems engineering experience.
- Get recognized for contributing to OSU’s AI innovation ecosystem.
- Internships begin in Summer 2025 with the opportunity to continue into the fall 2025.
- Hourly compensation and workload limits are governed by OSU student employment policies.
- Most work may be conducted remotely on a flexible schedule.
- Interns must sign a non-disclosure agreement to protect confidential research materials.
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