EXPOSED
The modal worker splits time between physical test work — installing strain gauges and telemetry, rigging test articles in wind tunnels and vibration rigs, running ground support equipment, troubleshooting instrumentation that reads wrong — and screen work like reducing test data, writing test reports, and maintaining calibration and configuration records. The screen half is squarely in AI's strike zone; the hands-on half needs a body in a hangar, on a test stand, or at a launch console. No license protects the role, but defense clearances, ITAR restrictions, and safety-critical procedure discipline keep the work onshore and human-supervised.
Roughly flat across the period, with year-to-year wobble.
Median pay $66,020 → $82,890 +0.4% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+8.1%
Percentage only. The projection counts a different population from the 11,280 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +8.1% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~900 openings a year on average, including replacing people who leave.
AerographerTest SpecialistTest TechnicianFlight TechnicianResearch MechanicAerospace MechanicAerospace AssemblerAerospace TechnicianWind Tunnel MechanicEngineering SpecialistEngineering TechnicianFlight Data TechnicianWind Tunnel TechnicianSystems Test TechnicianTest Equipment MechanicAvionics Test TechnicianAircraft Research MechanicInstrumentation TechnicianAltitude Chamber TechnicianData Acquisition TechnicianEngineering Test TechnicianFlight Readiness TechnicianAvionics Installation TechnicianAerospace Operations Technologist
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Instrumenting a test article — routing thermocouple leads, torquing fixtures to a rig, chasing a noisy channel back to a bad ground — resists automation, but the other half of the shift is data reduction in MATLAB/Python, filling out test logs, and drafting report sections against templates, all of which current tools already draft credibly, which is why this lands at 11 rather than the 15+ a pure test-floor role would earn.
Hands-on in uncontrolled environments The work happens in wind tunnel test sections, on vibration and thermal-vac stands, in hangars and on flight lines with hydraulics, cryogens, high-pressure pneumatics and energized ordnance circuits — uncontrolled enough to require PPE and safety watch protocols, and the 15 rather than 18 reflects that these are fixed company facilities with known configurations, not field repair in weather.
No licence, no signature requirement There is no PE stamp or FAA certificate on this role — the design engineer signs the test plan and the licensed engineer signs the analysis, while ASTM/NAS certifications and NDT Level II tickets are employer-preferred rather than statutory, so the 4 reflects credentialing that gates hiring but creates no personal legal exposure.
Meaningful discretion Calling a test abort, deciding whether a suspect strain reading is instrumentation or structure, and red-lining a procedure mid-run are genuine discretionary calls, but they escalate immediately to the test conductor and responsible engineer under documented safety-of-test rules, which caps this at 9 rather than the mid-teens where the ambiguous call stops with you.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (11/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (4/20) is whether the law requires a licensed human to sign. Trust premium (6/20) is whether buyers specifically pay for a person. Judgment and accountability (9/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 19 of this occupation's 45 points (42%).
Embodiment (15/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
Avionics Technicians SAFE
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 57/100, still EXPOSED.
If AI absorbs data reduction, report drafting and calibration record-keeping, the residual job becomes instrumentation troubleshooting, test-setup design and anomaly triage — the tier where a strain gauge reads wrong for a physical reason no dataset predicts. Task-mix shift, no law needed; visible already where test orgs push technicians toward test conductor roles.
Not a licensure route, but a documentation-accountability route: if FAA Part 21/145-style rules or DCMA quality clauses require a named, qualified human to sign test data as flight-worthy evidence — analogous to A&P sign-off on maintenance, or NADCAP AC7101 audits requiring named operator certification — the signature becomes personally attributable. Watch NADCAP and AS9100 revisions on AI-generated inspection/test records, and FAA guidance on ODA use of machine-generated compliance data.
If range-safety and test-readiness review practice formalizes a technician-level 'stop test' authority with named accountability — already implicit in NASA test conductor and launch console GO/NO-GO polling — and if DoD/NASA policy bars AI systems from being the authority for a NO-GO call on safety-critical test operations, the consequential-call ownership becomes explicit rather than customary.
If test programs shift toward more hardware-rich campaigns (hypersonics, new launch vehicle qualification, FAA Part 450 flight-test articles), the share of rigging, gauge installation and stand work per engineer rises. Also NASA/AFRL ranges retaining manual ground support equipment operation rather than autonomous test stands.
The limit. The strongest realistic gains are institutional (attributable sign-off on test evidence, formalized stop-test authority), not capability-based. Even fully realized, this stays a supervised-technician role rather than a licensed profession; screen-half erosion caps how far task_resistance can climb.
| Seattle-Tacoma-Bellevue, WA | 1,570 | $125,160 +51% |
| Los Angeles-Long Beach-Anaheim, CA | 690 | $98,940 +19% |
| Wichita, KS | 510 | $78,350 -5% |
| Palm Bay-Melbourne-Titusville, FL | 480 | $82,490 +0% |
| Denver-Aurora-Centennial, CO | 390 | $123,350 +49% |
| Dallas-Fort Worth-Arlington, TX | 280 | $98,800 +19% |
| Houston-Pasadena-The Woodlands, TX | 230 | $76,920 -7% |
| Boulder, CO | 220 | — |
| Seattle-Tacoma-Bellevue, WA | 1,570 | $125,160 +51% |
| Denver-Aurora-Centennial, CO | 390 | $123,350 +49% |
| Riverside-San Bernardino-Ontario, CA | 130 | $119,980 +45% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 45. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.