About The Position

This role will be a small intern team that will spend 10 weeks experimenting with how modern AI tooling (LLMs, OCR, and adjacent systems) could move our report authors from blank templates to populated first drafts that humans then review, edit, and finalize. The project starts from a blank canvas. The team will focus on one line of business and one document type, work directly with report authors to understand the current workflow, and deliver a working prototype plus an honest recommendation on whether to invest further. The project will be shaped to align with the team's strengths and interests – what you see here is the starting point, not the finish line.

Requirements

  • High school seniors through rising college juniors with a serious interest in computer science or software engineering.
  • Curiosity, the habit of building things, and comfort sitting with ambiguity.
  • Experience teaching yourself something outside of class, breaking and fixing your own code, or having opinions about how software should be built.
  • Excellent communication skills
  • Fluency in English

Nice To Haves

  • If you've taught yourself something outside of class, broken and fixed your own code, or have opinions about how software should be built, we want to hear from you.
  • You should apply even if you don't tick every box. If the project sounds exciting and you can point to one or two things you've built or figured out on your own, that's enough to start a conversation.

Responsibilities

  • Translating a fuzzy problem into something a small team can actually ship in 10 weeks
  • Running interviews with report authors and stakeholders to understand the real workflow (not the one in the SOP)
  • Owning the team's plan – what we're doing this week, what we're not, what we've learned
  • Writing the things that keep a team aligned: short status notes, decision logs, a clear definition of "done"
  • Helping the engineering interns stay focused on the right problem, not just an interesting one
  • Co-drafting the end-of-summer recommendation: what we tried, what we learned, what should happen next
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