A
AI Engineer Aircraft Data
AIP Holding, Inc.
2 hours ago
Full-time
On-site
Kentucky, United States
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).
Your mission 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.
Non-negotiable: you know aircraft 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.
What you'll own
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.
Who you are
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 traceability as part of the build, not overhead added afterwards — every output can be traced to a source, and you can say how confident the system is in it
You're comfortable being the only engineer in the room on your part of the system
You like data that fights back
You read AFMs for fun and you're only slightly embarrassed about it
Bonus
You own or share an aircraft
You've built something on ADS-B, registry, or fleet data
You've worked on performance, valuation, pricing, or marketplace models
Type rating, IR, or mid-training
Reading German or French (EASA-side registry and TCDS material)
Side projects with users
Shape of the engagement
Contract through Deel, hourly, invoiced weekly, paid on time
Full-time or part-time both work. If you're a final-year aero student who ships, two focused days a week on defined workstreams is a real option — say so in your application
Paid trial task first:
half a day at your hourly rate, on real (anonymised) data, before any longer engagement
Mutual NDA before the trial, since it touches customer records
Start: as soon as the trial clears
Why take it
The problem is genuinely unsolved. Nobody has built trustworthy structured ownership and maintenance history at scale in this market. The incumbents sell lists; we're building the record
A co-founder who has sold aircraft for three decades answering your domain questions in real time. That input is not available anywhere else
Paid properly from day one. Hourly, contracted, invoiced weekly, paid on time. No unpaid founder energy, no deferred anything
Real flexibility on hours, structured around defined workstreams rather than presence
#J-18808-Ljbffr
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).
Your mission 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.
Non-negotiable: you know aircraft 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.
What you'll own
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.
Who you are
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 traceability as part of the build, not overhead added afterwards — every output can be traced to a source, and you can say how confident the system is in it
You're comfortable being the only engineer in the room on your part of the system
You like data that fights back
You read AFMs for fun and you're only slightly embarrassed about it
Bonus
You own or share an aircraft
You've built something on ADS-B, registry, or fleet data
You've worked on performance, valuation, pricing, or marketplace models
Type rating, IR, or mid-training
Reading German or French (EASA-side registry and TCDS material)
Side projects with users
Shape of the engagement
Contract through Deel, hourly, invoiced weekly, paid on time
Full-time or part-time both work. If you're a final-year aero student who ships, two focused days a week on defined workstreams is a real option — say so in your application
Paid trial task first:
half a day at your hourly rate, on real (anonymised) data, before any longer engagement
Mutual NDA before the trial, since it touches customer records
Start: as soon as the trial clears
Why take it
The problem is genuinely unsolved. Nobody has built trustworthy structured ownership and maintenance history at scale in this market. The incumbents sell lists; we're building the record
A co-founder who has sold aircraft for three decades answering your domain questions in real time. That input is not available anywhere else
Paid properly from day one. Hourly, contracted, invoiced weekly, paid on time. No unpaid founder energy, no deferred anything
Real flexibility on hours, structured around defined workstreams rather than presence
#J-18808-Ljbffr