← Risk register SOC 51-3091 · reviewed 2026-08-11

Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders

20,370 US workers · median $44,810/yr · Production

COOKED

The core loop — watching temperature and moisture gauges, adjusting roast or bake time, recording batch data, pulling samples — is exactly what process-control software and inline sensors already do better and continuously. What holds the job in place is physical: loading and unloading ovens and dryers, clearing jams, cleaning and changing over equipment, and catching a burnt or off-color batch by smell and sight on the plant floor. There is no license, no signature, and the buyer of coffee or crackers never meets you, so the only real moat is that a plant would need capital to fully automate handling, not intelligence.

10-year outlook: Headcount keeps sliding as new lines ship with closed-loop control and fewer tenders per oven; the survivors are the ones who also maintain the machine or own the food-safety paperwork.

US employment, 2019–2025-2.2%
20,83020,370 workers

Roughly flat across the period, with year-to-year wobble.

Median pay $31,590 → $44,810 +13.5% in real terms (nominal +41.8%, less ~25% US inflation over the period)

The job count is not the verdict

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

+0.6% 20,700 → 20,800 on the projections basis

Exposed, but growing

AI can already do a lot of these tasks, and the BLS still expects +0.6% 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.

~2,400 openings a year on average, including replacing people who leave.

One email if this score changes. Watch as many occupations as you like from the same address — no account, and nothing is sent on a schedule, only when a verdict actually moves.

Also known as — 24 job titles this covers

Titles reported by people doing this work, from the US Department of Labor's O*NET survey. If your job title is here, this page is about your work even though the name doesn't match.

DrierGunnerSmokerEnroberPeelmanRoasterHam CurerMeringuerBun PannerDrum DrierHam SmokerKiln FirerMeat CurerMilk DrierPizza CookRice DrierRoastermanCorn PopperFish SalterFish SmokerFruit DryerGrain DrierMeat SmokerNut Roaster

Score — 30/100 resistance

Holding it up: embodiment (13/20). Weakest point: liability shield (2/20).

Five dimensions, 0–20 each, summed. Higher means more protected. The arithmetic is shown so you can check it: 7 + 13 + 2 + 3 + 5 = 30. · Scored 2026-08-11, and re-examined when evidence accumulates rather than on a schedule.

Task resistance 7/20

Mixed — a routine tier and a judgment tier Setting roast profiles, watching drum temperature and bean color, logging batch weights and times, and pulling cupping samples are all already run by PLC recipe control and inline NIR moisture sensors in modern plants — a 7 rather than a 3 because the manual pieces (rake-out, screen cleaning, changeover between product runs) still need a person on the floor, and rather than a 12 because none of the decision-making part of the loop is beyond existing software.

Embodiment 13/20

Hands-on in uncontrolled environments You are inside a hot, dusty production space handling 50-pound totes and hoppers, climbing on dryer decks, clearing chaff and product jams, and hosing down equipment between runs — a 13 because it is genuinely hands-on and non-repeatable in position, but not higher because the plant floor is a fixed, engineered environment rather than the unpredictable outdoor or field conditions that push scores toward 18.

Liability shield 2/20

No licence, no signature requirement No state license, no certification exam, no operator signature attaches to a batch; food-safety exposure rests with the plant's HACCP plan and the QA/PCQI who signs it, and a 2 rather than 0 only reflects the employer-mandated allergen and GMP training you sit through.

Trust premium 3/20

Anonymous artifact production The end buyer of the coffee, nuts, or crackers has no idea a person tended the roaster; your only relationship value is that your foreman knows which line you can run without scorching, which is a 3 — internal familiarity, not a product anyone pays for.

Judgment & accountability 5/20

Executes defined procedures on defined inputs Calls are bounded by the recipe card and spec sheet — hold this temperature band, this moisture percentage, reject out-of-color batches — and a 5 reflects the real but narrow discretion to stop a run or dump a burnt lot before QA sees it, with a supervisor deciding anything past that.

Confidence: high · reviewed 2026-08-11 · how scoring works

What this job involves — and which parts are yours

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.

AI already does these at usable quality

These still need a person

Active moats on the surviving side: embodiment

How to future-proof this job

Training paths for your skill gaps: Khan Academy — mathematics, arithmetic through calculus free · Apprenticeship.gov — paid, employer-sponsored trade apprenticeships, searchable by trade and ZIP paid to train · Apprenticeship.gov — industrial maintenance and millwright programs paid to train · MIT OpenCourseWare — circuits, systems and diagnostic method, free and ungated free · Coursera — engineering and procurement courses, auditable without paying free to audit

All 35 skills ranked by how many jobs they open →

Where this experience transfers — occupations you could move toward

Computed from U.S. Dept. of Labor O*NET skill and knowledge profiles: high overlap with what you already do, a materially higher resistance score, no large jump in required training, and no licence you would have to start a new pipeline to get. Targets that pay meaningfully less, that are themselves COOKED, or whose own headcount is falling are excluded — a move into a shrinking trade is not an escape.

Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders EXPOSED · 42/100 · you already have ~80% of the skill profile

Skills to close: Mathematics

Refractory Materials Repairers, Except Brickmasons EXPOSED · 65/100 · you already have ~75% of the skill profile

Skills to close: Repairing, Equipment Maintenance, Troubleshooting, Equipment Selection

Refuse and Recyclable Material Collectors EXPOSED · 55/100 · you already have ~71% of the skill profile

Skills to close: Equipment Maintenance

What would move this back up — beyond any one person

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 42/100 — EXPOSED.

4 specific changes that would raise this score
  • already happening trust premium +3

    Specialty-coffee certification schemes that require named human roasters — e.g. SCA-certified roast profiles, Q Grader involvement, or 'craft roasted' claims on single-origin bags — expanding as a marketing category. Buyers in third-wave coffee and artisan bakery already pay a premium tied to a named human at the drum. This applies only to the small-batch tier, not to commodity roasting or industrial cracker lines.

  • plausible judgment accountability +4

    FSMA Preventive Controls rules already designate a Preventive Controls Qualified Individual who owns critical-control-point deviation decisions. If a plant assigns PCQI status and CCP release authority to the roast/oven operator rather than a separate QA manager — common in small plants where headcount is thin — the operator personally owns the hold-or-release call on a deviated lot.

  • plausible task resistance +3

    Task-mix shift within the two-tier structure: if inline NIR moisture and color sensors plus PLC recipe control absorb the gauge-watching and batch-logging tier, the residual job becomes profile development, sensory cupping/QA rejection calls, and changeover troubleshooting on aging equipment. Watch for job postings retitled 'roast technician' or 'process technologist' requiring sensory panel qualification.

  • unlikely liability shield +2

    Narrow route only: FDA acrylamide guidance or a state cannabis/nut-pasteurization rule requiring a named, trained individual to sign off on validated kill-step or roast-lethality records per batch. Cannabis drying/curing in states like California and Colorado already requires named responsible persons on batch records. This is documentation duty, not personal liability of the kind a license carries.

The limit. Ceiling is low. There is no license, no client relationship, and the strongest holds are capital cost of materials handling and food-safety paperwork — both erodible. Most upside concentrates in the small-batch craft segment, which is a minority of these 20,370 jobs; commodity drying and industrial baking lines have essentially no route up.

These are conditions, not forecasts — what would have to happen, not what will. Specific rules, cases and bills are named so you can go and check whether they exist and where they stand; verify before relying on any of them. Nothing here is legal or financial advice.

Where this work is, and what it pays there

BLS metro figures for 67 areas. The verdict above does not change by city — the rubric judges what the work involves, not where it happens — but pay and headcount do, and the national median hides a very wide range.

Most of these jobs

New York-Newark-Jersey City, NY-NJ 1,190 $47,110 +5%
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 460 $48,330 +8%
Chicago-Naperville-Elgin, IL-IN 440 $38,230 -15%
Dallas-Fort Worth-Arlington, TX 420 $38,060 -15%
Omaha, NE-IA 380 $43,410 -3%
Minneapolis-St. Paul-Bloomington, MN-WI 340 $48,000 +7%
San Antonio-New Braunfels, TX 340 $34,170 -24%
Los Angeles-Long Beach-Anaheim, CA 320 $43,940 -2%

Best paid

Baltimore-Columbia-Towson, MD 120 $78,750 +76%
Denver-Aurora-Centennial, CO 310 $62,890 +40%
Modesto, CA 70 $61,130 +36%

Percentages are against this occupation's national median of $44,810. Counts are jobs in that metro, not vacancies. Metros where the BLS suppressed the cell are absent rather than shown as zero.

Who is actually doing this — nobody, on the record

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 30. 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.

Read that as a gap in the reporting we can see, not proof of absence — the dispatch runs on English-language feeds and misses plenty. If you know of a case, tell us, or add a field report from inside the job.

Quick take — do you do this job?

Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.

Self-reported and unverified — a sentiment signal, not a survey. One response per person per occupation; you can change your answer.

Field reports — what people say has changed

No field reports yet. A written account takes a paragraph rather than a tap, goes to an editor before it appears, and is the one thing on this page the rubric cannot produce on its own.

File a field report

Concrete beats general: a tool that arrived, a task that moved, a headcount decision you watched happen. Don't include anything that identifies you or your employer if that would put you at risk.

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Kept current

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