Precision grinding machine setter-operator
AI Exposure Rank
7/100
Range 5–11/100 across source-weight sensitivity checks
Precision grinding machine setter-operator has an AI Exposure Rank of 7/100, meaning its work is more exposed to current AI capabilities than approximately 7% of Singapore occupations. The evidence currently points to limited direct change; this is a relative rank, not a probability of job loss.
Plant & Machine Operators & Assemblers·SGD 3,227/mo (2,740–3,882)·~1.0K workers in SG·Updated 2026-06-11
Relative AI exposure, not a prediction of job loss. Hiring, wages and role design depend on many forces this rank does not forecast.
Why This Score
11% of tasks overlap with current AI
1% human advantage from judgment & presence
46% demand buffer from the local labour market
AI usage 6pp above theoretical exposure
These factors interact with each other — the final score is not a simple sum of these bars.
The evidence behind this occupation's AI exposure, with human-work and demand context shown separately. Score stability: watch. How this works
Tasks AI can handle
With 11% AI task overlap (based on Felten AIOE and Anthropic Economic Index), the Precision grinding machine setter-operator tasks most exposed include: predictive maintenance scheduling, safety checklist automation, inventory management, and remote monitoring via sensors.
- • Record data from operations, testing, and production on specified forms.
- • Observe operation of equipment to ensure continuity of flow, safety, and efficient operation, and to detect malfunctions.
- • Clean, adjust, and maintain equipment, using hand tools.
O*NET tasks for this occupation with the most observed AI usage (Anthropic task data).
What AI can't do here
At 1% human bottleneck protection, the tasks that remain hardest to automate for Precision grinding machine setter-operator include: physical dexterity on job sites, real-time environmental adaptation, operating heavy equipment safely, and handling unexpected on-site conditions.
Main insulation channels: Non-routine work + Relational work — the work-context dimensions behind this occupation's human bottleneck.
Skills to focus on
Sources: Felten AIOE (2021), Anthropic Economic Index (2026), Pizzinelli et al. bottleneck model. Full methodology.
Singapore Now
Current labour market conditions and how they affect this role.
Still healthy locally. Hiring remains positive and retrenchment stays low, even if demand is not accelerating.
Vacancy
2.8%
↑ 16.7% YoY
Hiring
2.4%
vs 1.5% resign
Retrenchment
1.5
per 1,000 · low
Re-entry
78.1%
find work in 12mo· -4.5pp
Production & Transport Operators, Cleaners & Labourers · 2025 Q4
Top Industries
Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.
What You Can Do
Precision grinding machine setter-operator has some offset potential, but it depends on transition pathways holding up in practice and on workers clearing the main switching frictions.
Published transition support
Related roles you could transition to
Similarity-basedMetalworking machine setter-operator →
Metal heat treating plant operator →
Metal melter, caster and rolling mill operator →
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Dairy and confectionery products machine operator →
Compare within Plant & Machine Operators & Assemblers
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 7% of occupations · V8 AI Exposure Rank· GCE O-Level / Secondary
Raw scores
AIOE -1.052 · θ 0.524 · C-AIOE -1.012
Stability
watch · Optimistic 2% · Pessimistic 14%
Score range (best/worst case)
Exposure sensitivity 7–13% · Rank sensitivity 5–11/100 across source-weight sensitivity checks
Scoring basis
V8 AI Exposure Rank. A relative Singapore occupation index. It ranks AI task exposure; it is not a probability of job loss or a percentage of tasks.
Wage range (SGD/mo)
25th 2,740 · Median 3,227 · 75th 3,882
Evidence & sources
Data matching
direct · SSOC 81252
Real-world AI usage: +6% vs estimated
Data quality
medium evidence · 2 exposure sources · direct mapping
Capped at high · Final rating: medium · capped for sparse source coverage
100% weighted task match · 5% effective coverage
AI overlap by data source
Weights: aioe 48% · anthropic 52%
Tools & offset factors
What helps
- Nearby moves and published transition support look reasonably strong.
Worker profile & local context
- Vacancy rate is 2.8% and rose by 0.8 points from last quarter.
- Hiring read: recruitment is running above resignation (2.4% vs 1.5%).
- Retrenchment was low at 1.5 per 1,000 employees.
- 78.1% of retrenched workers re-entered employment within 12 months.
- Employer pressure is low, based on 1 recent Singapore-relevant company signals.
Worker profile
Gender mix
47% male / 53% femalePublished Singapore worker composition for the detailed occupation family 81 Stationary Plant & Machine Operators.
Employment structure
More self-employed49% employees, 51% employers or self-employed workers.
Work arrangement
Mostly full-time11% part-time and 89% full-time in 2025.
Age profile
Older-skewing4% aged 15 to 29, 27% aged 30 to 49, and 70% aged 50 or older.
Qualification mix
Non-degree heavyBelow secondary 35%; Secondary 28%.
Where this work is concentrated
Top planning areas
Jurong West, Woodlands, Tampines26% of workers in this occupation group live in these three planning areas.
Residential concentration
More concentrated39% live across the top five planning areas in the 2020 Census.
Commute pattern
Shorter commutesEstimated average commute 21.5 minutes. 14% take 46 minutes or more.
Role profile
How this role's work breaks down across key dimensions. This is a general profile, not an individual measurement.
Workflow dimensions (0 = low, 1 = high)
How this changes by career stage
Career stage can change the task mix and human context. These directional profiles are illustrative, not occupation-level forecasts of hiring or displacement.
Frequently asked questions
Will AI replace Precision grinding machine setter-operator?
Precision grinding machine setter-operator has an AI Exposure Rank of 7/100, meaning its work is more exposed to current AI capabilities than approximately 7% of Singapore occupations. The evidence currently points to limited direct change; this is a relative rank, not a probability of job loss. AI Exposure Rank: 7/100 (Very Low). Median wage: SGD 3,227/month.
What is the AI exposure rank for Precision grinding machine setter-operator?
Precision grinding machine setter-operator has an AI Exposure Rank of 7/100, rated Very Low. It ranks higher than approximately 7% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Precision grinding machine setter-operator?
Precision grinding machine setter-operator has modeled transition pathways to related occupations. The strongest adjacent pathway is Metalworking machine setter-operator, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Precision grinding machine setter-operator salary compare in the live market?
Precision grinding machine setter-operator earns a median gross wage of SGD 3,227/month in the live market (25th-75th percentile: SGD 2,740-3,882). This is 28% below median across all 562 scored occupations, and 21% above group median within Plant & Machine Operators & Assemblers occupations.