AI Engineer — Aircraft Data

Remote Contract via Deel, hourly, invoiced weekly Must partially overlap US hours

We're building the transaction and ownership intelligence layer for business aviation — the system of record for what an aircraft is, who owns it, what it's worth, and what happened to it.

Currently in stealth. In closed beta with a small group of brokers and principals who use the product on live deals and tell us to our faces when it's wrong.

Two founders. One with 27 years in tech, media and SaaS, and multiple exits. One with 30 years in business aviation. You'll work directly with both, plus a fractional CTO (PhD, ML).

Turn the ugliest data in aviation into a product people pay for.

Aircraft data is scattered, contradictory, half-scanned, and older than most of the people reading this. Registries disagree with logbooks. Logbooks disagree with each other. Serial numbers get transposed. Airframes change registration three times and countries twice.

Your job: make it structured, trustworthy, and queryable — fast.

Use whatever works:

• LLM extraction pipelines
• OCR on 1970s carbon-copy maintenance entries
• Entity resolution across registries
• Scrapers
• Embeddings and retrieval
• Valuation and performance models
• Agents that do the boring parts
• Something nobody's tried

If it works, we double down. If it doesn't, we kill it that week.

We want a builder
, not a researcher. We're not writing papers. We're shipping into live transactions where a wrong ownership record kills the deal and the customer relationship with it.

This is aviation software. You cannot write it without understanding the aircraft.

Every panel we ship encodes a technical judgement. A payload-range comparison is not a lookup of two published figures — it's a tradeoff under fuel reserves, ISA deviation, cruise setting and runway performance. A valuation is not a regression on hours — it's engine programme status, damage history, AD and SB position, avionics currency. An engineer who doesn't understand what the numbers mean will build something that computes cleanly and is wrong at the design level.

That is not caught by testing, and it is not caught by us reviewing the PR.

You need at least one of:

• A pilot licence — PPL or above, any authority
• A degree in aeronautical or aerospace engineering
• Real operational time inside aviation: MRO, CAMO, avionics, flight ops, flight test, or an OEM

And you need to talk about an airframe without a glossary. TBO. AD compliance. SB status. Damage history. Total time vs. TSO. Part-ML vs. Part 91. Payload-range tradeoff. What "engine on programme" does to a valuation. The difference between an AFM and an AMM — and why only one of them can be quoted to a buyer.

No aviation background → not a fit.

• The ingestion and extraction pipelines that feed the platform — registry data, scanned logbooks, maintenance records, flight data
• The performance and comparison logic that sits on top of it
• LLM-in-the-loop systems where accuracy is auditable, not vibes
• Evaluation: knowing the model is wrong before the customer does
• User-facing features off the back of it, not just backend plumbing

You'll own this layer end to end. You won't own the whole architecture — the surfaces, the warehouse and the product direction sit with the technical co-founder, and there's a fractional CTO on call for the ML questions. What you get is the hardest, least-solved part of the system and the authority to decide how it gets built.

• You've shipped production software with AI in it — not a demo, not a notebook
• You treat LLMs as unreliable components you engineer around, not as magic
• You'd rather ship one thing that's right on Friday than five that are nearly right in October. You cut scope, not corners
• You treat…