EXPOSED
Much of the daily work is document- and code-heavy: writing safety analysis reports, assembling NRC licensing submittals, running neutronics and thermal-hydraulic codes, and tracking design changes — all areas where AI already drafts, cross-references regulations, and sets up parameter studies competently. What resists is the accountable engineering judgment: validating that a model matches a physical plant, defending assumptions to the NRC, and signing off on decisions where the failure mode is a radiological release. Plant-side work — outage support, walkdowns, radiation shielding verification, fuel handling oversight — keeps a real physical component that robotics cannot cover.
Roughly flat across the period, with year-to-year wobble.
Median pay $113,460 → $133,970 -5.5% 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
-1.1% 15,400 → 15,300 on the projections basis
Shrinking, but not obviously because of AI
The BLS projects -1.1% by 2034, but at 57/100 this work is only moderately exposed — not the profile of a job current AI can simply do. Occupations shrink for many reasons, and the score does not point at automation as this one's cause.
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.
~800 openings a year on average, including replacing people who leave.
EngineerNuclear OfficerSafety EngineerSystem EngineerNuclear EngineerNuclear MechanicReactor EngineerWeapons DesignerWeapons EngineerRadiation OfficerRadiation EngineerEngineering OfficerNuclear ElectricianStructural EngineerRadiological EngineerRegulatory CoordinatorSurvivability EngineerSystem Safety EngineerAtomic Process EngineerNuclear Design EngineerNuclear Safety EngineerSteam Technical AdvisorCore Measures AbstractorNuclear Process Engineer
Holding it up: judgment & accountability . Weakest point: trust premium .
Mixed — a routine tier and a judgment tier At 12 rather than 8, MCNP/RELAP5 input decks, 10 CFR 50.59 screening write-ups, and Chapter 15 accident-analysis chapters are all templated enough that AI drafts credible first passes, but reconciling code output against as-built plant configuration, resolving NRC requests for additional information, and interpreting anomalous core-follow or dosimetry data still require someone who has walked the containment.
Some physical or field component A 9 reflects the split: most weeks are at a workstation, but fuel-shuffle oversight, in-containment shielding walkdowns during refueling outages, ALARA planning at the job site, and hot-cell or reactor-bay work under an RWP put you in a radiological area with a TLD on your badge — controlled environments, not a rooftop or a trench, which is why it isn't 14.
Licensed human required and personally liable 13 comes from the PE stamp on safety-related design and NRC-recognized roles — the licensing basis submittal names a responsible engineer, and 10 CFR Part 21 defect reporting attaches personally — but a large share of nuclear engineers work under a utility's or vendor's Appendix B quality program without ever stamping a drawing, which caps it well below the 18-20 of a structural PE.
Exists to be accountable for ambiguous calls 15 is earned on calls like whether a degraded ECCS condition is still within the licensing basis, whether a 50.59 change needs a license amendment, and what margin to accept on peak clad temperature — decisions made in the gap between code output and reality, where being wrong is a radiological release and the regulator will ask you personally to defend the assumption.
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 (12/20 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 (13/20) is whether the law requires a licensed human to sign. Trust premium (8/20) is whether buyers specifically pay for a person. Judgment and accountability (15/20) 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 36 of this occupation's 57 points (63%).
Embodiment (9/20) 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.
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 72/100 — SAFE.
SMR and advanced-reactor licensing (NuScale, TerraPower, X-energy) creating first-of-a-kind design work where codes are unvalidated against any operating plant — the accountable call becomes justifying model applicability and defending novel assumptions in ACRS hearings, which AI cannot own
Task-mix shift: as AI absorbs SAR drafting, regulatory cross-referencing, and parameter-study setup, the residual role concentrates in code validation against plant data, benchmarking, uncertainty quantification, and NRC RAI defense — a genuinely two-tier occupation where the surviving tier is the judgment tier
NRC guidance or a rule change (e.g., an update to 10 CFR 50 Appendix B / NQA-1 quality assurance requirements, or an RIS on AI use in licensing submittals) that requires a named licensed Professional Engineer or designated Responsible Engineer to personally attest that AI-generated safety analysis, code inputs, and 50.59 evaluations were independently verified — with individual accountability under NRC enforcement and 10 CFR 50.9 completeness-and-accuracy obligations
A construction wave of new-build reactors shifting headcount toward field engineering, ITAAC verification, startup testing, and as-built walkdowns — physical, unstructured plant work rather than desk analysis
State licensure boards closing the industrial exemption that lets many nuclear engineers work unstamped: if states following NCEES model law require PE stamps on nuclear safety-related design documents, sign-off becomes a personally liable act rather than a corporate one
The limit. Trust premium has no realistic route: the buyer is a utility or the NRC, and neither pays for human authorship as such — they pay for a defensible signature, which is the liability lever, not a trust premium. Embodiment gains are cyclical and tied to construction, so they can reverse.
| Virginia Beach-Chesapeake-Norfolk, VA-NC | 1,260 | $110,510 -18% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 840 | $180,360 +35% |
| Chicago-Naperville-Elgin, IL-IN | 710 | $134,510 +0% |
| Portland-South Portland, ME | 370 | $120,430 -10% |
| Richmond, VA | 350 | $140,460 +5% |
| Seattle-Tacoma-Bellevue, WA | 330 | $161,050 +20% |
| Charlotte-Concord-Gastonia, NC-SC | 240 | $131,850 -2% |
| Knoxville, TN | 240 | $179,660 +34% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 840 | $180,360 +35% |
| Knoxville, TN | 240 | $179,660 +34% |
| Seattle-Tacoma-Bellevue, WA | 330 | $161,050 +20% |
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 57. 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.