Alcor is opening a small cohort of builder-interns to work alongside our M&A team — learning how real deals actually move, then shipping the AI tools and applications that will run them. You won't be fetching coffee. You'll be writing code that handles confidential information memoranda, target screening, diligence, and deal modeling.
An LLM-powered system that scans thousands of private companies against a buyer's investment thesis — sector, size, geography, growth signals — and surfaces ranked candidates with reasoning attached.
A diligence assistant that ingests confidential information memoranda, 10-Ks, financial models and data-room files — extracts the numbers that matter, flags the red ones, and drafts the first-pass investment memo.
Pull comparable transactions and trading multiples from public filings and proprietary deal data, normalize them, and serve a valuation range bankers can actually defend in front of a client.
An interface that lets a deal team query an entire VDR in natural language — every contract, every customer concentration, every change-of-control clause — with citations back to the source page.
Graph-based matching between sell-side mandates and strategic & financial buyers — modeling synergies, stated criteria, and historical behavior to predict who will actually engage and at what price.
Every intern proposes one tool, builds a working prototype, and presents it to the partners in week ten. The strongest ones get adopted into our actual production stack. Yes, really.
The M&A side
From the first teaser through LOI, diligence, definitive agreement, and close. You'll sit in on live deals under NDA.
Quality of earnings, working capital, customer concentration, retention curves, contract risk — the questions a real banker is paid to ask.
DCF, comparable companies, precedent transactions, LBO math. How analysts actually build models and where the assumptions hide.
Reps & warranties, earnouts, escrows, indemnification — the structures that make deals fair, or unfair.
The AI side
Prompt design, tool use, function calling, and the practical engineering of getting LLMs to do work that gets shipped.
Chunking, embeddings, hybrid retrieval, reranking, evaluation — and when retrieval is the wrong answer.
Multi-step reasoning, planning, error handling, and the harder question of which problems are actually worth automating.
Git, code review, deployment, evals, observability. We'll train you on the parts of being a software engineer that school doesn't.
Crash course in M&A — process, vocabulary, financial models. Crash course in modern AI tooling. Pair programming from day one.
You'll be assigned to a working tool team. Shadow live deals. Write code that goes into the actual product. Daily standups with engineers and bankers.
Take ownership of one feature, end-to-end. Scope it, build it, evaluate it, deploy it. Get torn apart in code review. Make it better.
Present to partners and senior engineers. The best work goes into production. Strong interns receive return offers — full-time or next-summer.
We are looking for brilliant self-starters, high achievers, technical chops, curiosity about finance, willingness to ship and team players
M&A is the most human business in finance. AI is the most consequential technology in our lifetime. The people who learn both — early — will define the next decade of deal-making.
Two minutes. No résumé padding, no cover-letter clichés. Show us what you've built and how you think — we read every word.