Internship Iridium Jobs (NOW HIRING)

Software Engineering Placement · New York

Iridium is building the infrastructure that scales invoice finance. So that every business can access the money they’re already owed.

Backed by strategic institutional investors.

Context

Every business that sells to another business has invoices. Every time goods are delivered or a service is rendered, an invoice exists. In aggregate, US businesses are sitting on $7 trillion of this.

Contracted. Documented. Due.

It is, in theory, the most abundant form of collateral in existence. And yet less than 1% of it is ever used to secure financing.

Lending against invoices isn’t new. Lenders have known for decades that a confirmed invoice from a creditworthy buyer is about as safe a bet as commercial lending gets - you’re underwriting a transaction that already happened. But doing it safely is brutally, structurally manual, and that burden doesn’t stay flat as you scale. It compounds. To lend against an invoice, a lender has to do three things every single time:

01

Is this borrower legitimate, is the debtor actually going to pay, and what does the risk look like across the whole lending book when you add this invoice in?

02

Verify

Is this invoice real, was the work actually done, and does the backup documentation across email threads, supplier portals, and accounting systems hold up?

03

Collect

When does the money actually arrive, and who is chasing the invoices that aren't paying on time?

Lenders process thousands of invoices every week. Each one needs all three and there is no shortcut that doesn’t introduce risk. The temptation to sample, verify 20% and fund everything, is how the industry has lost billions.

BlackRock's HPS

$430M

lost to fabricated invoices that cleared multiple audit checkpoints.

$715M

lost to invoices that turned out to be misrepresented.

Fraudulent invoices look exactly like real ones, until they don’t.

Why nothing has fixed it

You’d think LLMs would have solved this by now. They haven’t.

01

02

Even if the models were good enough, the harness doesn’t exist.

No system that maintains a living view of a lending book and updates it as every new invoice changes the risk profile. No engine that gets smarter with every invoice funded and every payment collected. No voice layer that can collect on invoices the way a human would, with the right context, the right tone, and the right moment. Automated reminders recover a fraction of what a well-timed, informed conversation does.

What you will work on

One hard problem at the core of the company. Not a rotation.

01

Invoices don’t get paid the way the terms say they will. Every debtor has a rhythm - who pays early, who pays at 74 days regardless of what the contract says, whose behaviour shifts before they stop paying entirely. You will build the systems that learn that rhythm from real payment histories and turn it into something the rest of the platform can act on.

02

Collection Policy Generation

Once you can predict how an invoice will actually be paid, the question becomes what to do about it. You will work on generating the collections policy itself - when to reach out, through which channel, and how hard - and on feeding the same signal back into how we underwrite the next invoice. This is a live problem for our lenders, not a research exercise.

03

Across The Stack

Next.js and React on the front, Python and FastAPI on PostgreSQL and AWS behind it, with heavy use of agentic AI, evaluation pipelines, search and retrieval, and time-series data. You will not be handed one narrow slice. You will follow the problem wherever it goes.

04

Forward Deployed

You will get real exposure to the lenders we serve. Sitting in on how they work, seeing which of your assumptions survive contact with an actual credit team, and bringing that back into what you build. Most engineers wait years for that access.

What we are looking for

We value range and trajectory over years of experience. Above all we look for agency, humility, and optimism.

Role

An engineer on the founding team with real ownership of work that ships. Not a rotational internship, and not shadowing.

Duration

Around four months over summer 2027. We will fit the exact dates around your course.

Next.js / React, Python / FastAPI, PostgreSQL, AWS. Agentic AI, evaluation pipelines, search & retrieval, time series.

Who it is for

Students at any university on a placement year, year in industry, or summer internship. Solid coding ability, comfortable reading documentation, and genuinely curious about AI and LLM systems.

Location

In-person in New York. We sponsor visas and cover relocation for international applicants.

We hold a high bar. If we don’t find someone we’re genuinely excited by, we will leave the role unfilled rather than fill it with the wrong person. This has to be a good match on both sides.

A note to you

The average person spends 40 years in their career. That’s a long time to spend on something you don’t truly love. The people that care deeply about their work are exactly who we’re building this with.

We’re not looking for someone who has done this before. We’re looking for someone who thinks higher up the stack than their experience would suggest. Someone who understands that the tools available today mean the only real limit is how boldly you’re willing to think. The builders who will win in this era aren’t the deepest specialists. They’re the ones with the range to reason across systems and the courage to build things that haven’t been built before.

We believe greatness builds with time, not by chasing easy wins. The people we want dive head first into the deep end, with an almost delusional belief that they’ll figure it out, even when nothing yet suggests they will.

This journey is hard. It is also one of the most rewarding things you will ever do. The people who join us early are the DNA of what Iridium becomes. We will give you every resource, every bit of support, and hold you to the highest bar we know how to set. We expect you to hold us to the same.

Also we’re pretty good company. It’s a long road and we intend to enjoy every bit of it.

Software engineering placement. New York. In-person.