Lead a Team, Build Your Portfolio, and Get Paid: Apply Today for June & July Sessions

💡AIVIA Live Apply to 2025 Virtual-Internships
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Ready to elevate your AI skills and leadership?

We’re seeking motivated Team Leads for our upcoming Virtual-Internships (June & July). Guide a team to replicate, extend, and present cutting-edge AI research or build an innovative AI-based online product in one of five exciting areas:

  • Recommender & Decision Systems,
  • Language Models & Generative AI,
  • Computer Vision & Multimodal AI,
  • AI in HealthCare & Medicine, or
  • Domain-Specific AI.

Leads with compelling project pitches who secure at least 4 participant sign-ups receive immediate approval to launch! We anticipate running 1–3 teams per AI domain, so pitch early and gather your team quickly!


How It Works

  • Select a Project
    • Select a research paper from one of the five categories or explore our recommended list. We’ll review your selection to confirm suitability within our structured framework.
    • Alternatively, you may propose an AI-based product idea, which we’ll help refine before final approval.


  • 3-Phase Project Structure: Each project follows our structured three-phase model. Objectives are clearly defined yet flexible enough to guide your progress and enable evaluation.

    • Research-Based Projects:

      • Phase 1: Research & Planning – Understand and replicate methodologies.
      • Phase 2: Extend & Innovate – Expand on existing research with original analysis or new features.
      • Phase 3: Present – Share your results and contributions in a final showcase.

    • Product-Based Projects:

      • Phase 1: Architect & Plan – Define product objectives and design milestones.
      • Phase 2: Develop & Test – Build, refine, and test your AI-based product.
      • Phase 3: Present – Demo your innovative product in a final showcase.


  • Flexibility & Support
    • Meet with your team as needed. One mandatory meeting per week with STEM-Away mentors for progress check-ins and Q&A.
    • 3-week total timeline to keep things fast-paced (yet still manageable).


Why Join as a Lead?

  • Build a Killer Portfolio: Lead a project that highlights both your technical prowess and leadership skills.
  • Get Paid: Receive $100/week (paid weekly upon successful completion of each check-in). Plus, an additional $50 award per week goes to the team member with the strongest contribution. We’re also in discussions with company sponsors who may offer additional financial incentives in the future.
  • Flexible: Choose the paper that excites you, set your team’s pace, and focus on what you love.
  • Stand Out: Gain leadership and research experience that makes you a top candidate for future internships, jobs, or grad school.


What We’re Looking For

  • Expert-Level Evaluation in your chosen category, demonstrated by achieving “Expert” or “Intermediate” in at least one AIVIA project within that category.
  • College-Level Education - eam leads should generally be at a college level or higher. High school students may apply, but please note that spots are very limited and applications are accepted only through the Discovery Session Package.
  • Proactive Mindset – ready to lead a small team, solve challenges, and deliver results.
  • Passion for AI – excited to dive deep into research and push the boundaries of what’s possible.
  • Selection Process: We may invite shortlisted candidates to a brief (15-minute) call to discuss leadership approach and project ideas before making final decisions. If multiple candidates choose the same domain, selections are based on alignment, extension ideas, and leadership potential.


How to Apply

  • Click on @stemaway
  • Select Message in the user card that appears.
  • Use the Application Template below (no exceptions). Applications that don’t follow this template will not be considered.
  • Deadline: Rolling. Spaces are limited, so apply early
  • Spaces for Leads are limited—secure your spot and get ready to build something awesome!

By combining your own expertise with AIVIA’s structured learning approach, you’ll learn, earn, and lead—all while working on projects that push the cutting edge of AI. If this sounds like you, take the leap and apply as a Team Lead today!

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Application Template

Subject: Lead Application

Body:

  • Chosen AI Category
  • Relevant AIVIA Expert Evaluation
  • Proposed Paper or Product
  • Brief Pitch (2–3 lines)
  • STEM Level
  • Availability


Application Details

  • Chosen AI Category: Please choose one of the following: Recommender & Decision Systems, Language Models & Generative AI, Computer Vision & Multimodal AI, AI in Healthcare & Medicine, or Domain-Specific AI (other than Healthcare & Medicine).
  • Relevant AIVIA “Expert” Evaluation:
    • Which project did you earn your “Expert” rating in? Please share the project URL.
    • Note: The project must be from your chosen category. Selecting a more complex project may improve your chances, but a strong evaluation on any relevant project is sufficient.
    • You may skip this question if you selected Domain-Specific AI (other than Healthcare & Medicine). We’ll create a relevant project in that domain and send you the details.
  • Proposed Paper or Product: Clearly state your selected research paper or product idea. If you already have a specific research paper in mind, list it here. Otherwise, pick from our suggestions below (check back for new ones!)
  • Brief Pitch: (2-3 lines): Briefly explain your unique approach, how you’ll extend or analyze existing research, or your idea for an innovative AI product.
  • STEM Level: Indicate your current academic or professional status (e.g., Undergraduate Junior, Undergraduate Senior, Master’s Student, PhD Student, Junior Professional, etc.).
  • Availability:
    • Pick a starting Monday in June or July. If flexible, say something like “any Monday,” “any Monday in June,” etc.
    • Please note that you are committing to a 3-week timeline with one mandatory meeting per week.
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LLM-Rec: Personalized Recommendation via Prompting Large Language Models

Link: arXiv:2307.15780v3

Why It’s Good

  • Cutting-Edge: Leverages large language models (LLMs) for personalized recommendations, a rapidly growing area in AI research.
  • Code Availability: Code is not publicly available. Paper can be used as guide only.
  • Versatility: The paper explores multiple approaches to integrating LLMs into recommender systems, providing ample opportunities for experimentation and innovation.

Potential Extensions

  • Domain Adaptation: Apply or fine-tune the approach on a different dataset (e.g., MovieLens, e-commerce data).
  • Prompt Engineering: Explore how different prompts or few-shot techniques impact recommendation quality.



Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Link: arXiv:1910.10683

Why It’s Good:

  • Cutting-Edge: Introduces the Text-To-Text Transfer Transformer (T5), a unified framework for solving diverse NLP tasks like summarization, question answering, and classification. It’s a cornerstone in modern NLP research.
  • Code Availability: Code, datasets, and pre-trained models are publicly available on GitHub.
  • Versatility: The text-to-text format allows T5 to be applied to a wide range of tasks, making it a powerful tool for experimentation.

Potential Extensions:

  • Task Adaptation: Fine-tune T5 for new tasks like sentiment analysis or domain-specific text processing.
  • Prompt Engineering: Experiment with prompts to improve zero-shot or few-shot learning performance.
  • Efficiency Improvements: Explore techniques like model distillation to make T5 more efficient for real-world applications.