F&L Search
Founding Engineer - Full Stack (TypeScript, Python, Postgres)
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F&L Search - Specialist Market Matching System Engineer
F&L Search places investment strategists, analysts, and sales people with hedge funds, banks, and research houses. Over the past year, it has built an intelligence database of 35,000 people and a team of AI agents that source, screen, write, and file under written instructions and review. The agents already do most of what a research team used to do. The product built on top of that needs an engineer who owns it.
What You Would Build
Matching
The core of the product: scoring candidates against live jobs on quality and fit, from CVs, calls, research, and track record, so the best people surface first and the reasons are shown, not asserted.
Enrichment
Company pages that grow themselves: every firm in the market with its people, moves, mandates, and what has been learned about it, pulled from documents, calls, and public sources and kept current.
A Workflow that Leads the People
Today a recruiter decides what to do next; here the system does. It should put the right next action in front of a recruiter or an agent, with the evidence attached, and record what happened. The people respond to the workflow, not the other way round.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Client Portals that are Actually Used
Each client sees their searches, the people in them, where each person stands, and what is coming, live. Functional, not a brochure. The boundary between the internal database and what a client can see is the product's promise, designed once and enforced by tests.
Production
The database, backups, monitoring, and uptime that paying clients depend on, and the security conversations with banks and funds before they sign.
Why Now, and Why This is Only the Start
This works in investment research recruitment because that market is deep, specialist, and poorly served by generic tools. The architecture is not specific to it. A database of people and firms, agents that enrich it, matching, a workflow, and a client window apply to any specialist market where who-knows-whom decides outcomes. The plan is to prove it here, then repeat it sector by sector. The founding engineer builds the first one and owns the template.
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Who This Suits
- Five or more years building and running B2B software in production, ideally as an early engineer somewhere that grew.
- Strong in at least two of TypeScript and Next.js, Python, and Postgres, and willing on the third. Built on Supabase today.
- Has shipped matching, ranking, search, or data-enrichment features, or wants to make them the centre of their work.
- Comfortable building with AI coding agents as the method, and sharp about reading their output critically.
- Serious about data boundaries and tenancy. Experience with financial clients or sensitive personal data is a strong plus.
- Able to work in London regularly. Right to work in the UK is required.
This will suit someone who wants to own a product and a real stake in where it goes. It is less likely to suit someone who wants a large team around them, a narrow specialism, or a pure front-end role.
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