Real production-grade experience · application-gated
You can't get experience without a job. You can't get a job without experience. We build the experience that breaks the loop.
Spend 8–16 weeks building the same kind of AI systems banks and wealth managers ship — in a training environment that mirrors their architecture and standards, on synthetic data — reviewed round by round by senior engineers and scientists. Whether you come from CS or finance, you walk away with production-grade work, a portfolio recruiters respect, and proof an employer can verify.
Two tracks · engineering and fintech · fully remote.
Why now · the problem
A degree alone no longer sets you apart.
You're stuck in the cold-start paradox: every role wants experience, and no one will give you the experience to get it. Meanwhile AI is quietly absorbing the junior work that used to be how people broke in.
The people who win the next decade are the ones who can show, not claim, that they can do the work.
What this is
Real work, senior review, proof you can verify.
You build the real thing
The same kind of AI systems banks and wealth managers ship — in a dedicated training environment that mirrors real fintech-AI architecture and standards, on synthetic data. You never touch client systems or real customer data.
Senior people review it
Your work is reviewed round by round by senior engineers and scientists who do this for a living — the way real teams ship. The experience is real; the data is not live.
You leave with proof
Everything you build is backed by a verification page an employer can check. Fellowships are application-gated — we admit people we're confident can finish production-grade work and prove it.
Two tracks · every major covered
Which one is you?
Engineering Track
You can already code but can't get real experience — especially if you're aiming at fintech, quant, or AI engineering.
You build the systems: production-grade AI features, merged code, and architecture you can defend.
Skill floor: you can write code. ML scaffolding is provided where you need it.
FinTech Track
Finance, econ, or business background with some Python — IB, sales & trading, asset & wealth management, risk, compliance.
Banks are racing to put AI into KYC, AML, compliance review, and portfolio analysis — and can't find people who understand both sides. You build that rare ability.
Skill floor: you can load a CSV into pandas, write a for-loop, and call a library function. Heavy ML is scaffolded; every deliverable gets expert review.
What you walk away with
What you take home is work — not promises.
A production-grade AI feature, merged & senior-reviewed
Real code you can defend — plus a GitHub showcase repo and a recorded architecture walkthrough of the system you built.
An AI accuracy & hallucination evaluation you designed
You define "correct," build the test set, and quantify the error rate and its business cost — the work banks are racing to staff.
A verification page an employer can check
Independent proof of what you built and that it was senior-reviewed — answered for employers within 48 hours.
A decision-support view tied to a real finance decision
A case-study portfolio of a use case you owned, with a recorded walkthrough recruiters can watch.
A recruiter-reviewed portfolio, résumé & LinkedIn
Plus two mock interviews with real feedback that sharpen how you present your proof.
A co-supervised technical paper or report you author
Coaching and editorial review only — never ghostwritten, never a guaranteed publication. The work and the credit are yours.
Your mentors
For a new program, the strongest proof is who reviews your work.
Lona Yu
Former Amazon applied scientist in Amazon's Customer Trust org (fraud and abuse). Her anomaly-detection and anti-money-laundering research was named one of Amazon Science's ten most-viewed publications of 2024 and presented at WSDM 2024. Founder-in-Residence at AI2 Incubator.
Wealth-management mentor
A CFP® professional (Series 66) with years of experience as a private wealth manager and regional vice president at established U.S. wealth-management firms. Co-mentors the fintech side.
Joanna Fang
A computer scientist and published researcher in human-computer interaction — the study of how people and technology work together — at Virginia Tech. Volunteers her time to help build and run the Ledgerline fellowship.
How to join
Application-gated — there's no checkout, so we talk first.
① Apply
A short form below, or email us — whatever's easiest.
② 1:1 fit call
A quick conversation to confirm the track, plan, and a seat fit you.
③ Start
If it's a match, we invoice and you begin with the next cohort. 14-day full refund still applies.
④ Demo Day
Present your project to mentors and invited engineers, and leave with verifiable proof.
Three plans, by length and depth
Build
Get real, reviewed work into the world and prove it: production-grade work + senior code review + a verification page + Demo Day.
Accelerate
Everything in Build, plus 1:1 senior mentorship, two mock interviews, and a career-asset pack: portfolio, résumé, and LinkedIn.
Scholar
The deepest plan: an independent technical paper or report you author, co-supervised by a researcher (coaching and editorial only — never ghostwritten, no guaranteed publication).
💬 Plans and pricing are set in a 1:1 consult (tailored to your track and depth). There's no checkout here — we invoice only after it's a match, and you have a 14-day full refund once you start.
Questions · FAQ
Straight answers.
Is this a job or an internship?
No. Ledgerline is a paid educational fellowship. You join as a Fellow — not an employee, intern, or contractor — and you don't become one through the program. The plan names describe how deep the program goes, not a title you earn.
Do you guarantee an offer, referral, or placement?
No. We sell real, senior-reviewed experience and verifiable proof — never jobs, offers, referrals, or placements. What you do with the proof you build is yours to earn.
How much time does it take?
About 10–15 hours a week, fully remote, over an 8–16 week arc depending on your plan.
Do I need to be a strong coder for the FinTech track?
No. If you can load a CSV into pandas, write a for-loop, and call a library function, you can do the FinTech track; heavy ML is scaffolded and every deliverable gets expert review. The Engineering track is the one that assumes you can already code.
What do I actually leave with?
Production-grade work reviewed by senior engineers and scientists, a verification page an employer can check, a Demo Day presentation, and track-specific artifacts — merged code, an AI evaluation you designed, or a co-supervised technical report. See What you build.
What does it cost? How does billing work?
There are three plans — Build / Accelerate / Scholar — priced by length and depth. Plans and pricing are set in a 1:1 consult. There's no checkout on this site: we invoice after it's a match, with a 14-day full refund once you start.
Do Fellows touch real customer data?
Never. You work in a training environment that mirrors production standards, on synthetic data only — never client systems or real customer data. Nothing in the fintech track implies licensing, Series exams, or registered/financial-advisor status.
Founding cohort · limited seats
info@ledgerlinefellowship.com
We reply within a couple of days · application-gated · 14-day full refund
Break the loop. Build real proof.
Email us at info@ledgerlinefellowship.com, or leave your details below — we'll reply to set up a short fit call. 14-day full refund once you start.