Manufacturing engineer
AI Exposure Rank
58/100
Range 52–70/100 across source-weight sensitivity checks
Manufacturing engineer has an AI Exposure Rank of 58/100, meaning its work is more exposed to current AI capabilities than approximately 58% of Singapore occupations. The evidence currently points to workflow redesign; this is a relative rank, not a probability of job loss.
Professionals·SGD 4,589/mo (2,596–6,860)·~2.1K workers in SG·Updated 2026-07-17
Relative AI exposure, not a prediction of job loss. Hiring, wages and role design depend on many forces this rank does not forecast.
How This Rank Is Built
The source percentiles are combined using the displayed reliability weights, then ranked against all 562 Singapore occupations. This is not a percentage of tasks and not a job-loss probability.
Likely job pathway
Workflow redesign
Current demand context
Mixed current demand
Pathway and demand are reported beside the exposure rank. Current demand does not change the rank.
The evidence behind this occupation's AI exposure, with human-work and demand context shown separately. How this works
Tasks AI can handle
With 58% AI task overlap (based on Felten AIOE, Anthropic Economic Index, Eloundou GPT exposure, and ILO occupational exposure), the Manufacturing engineer tasks most exposed include: running standard statistical analyses, generating charts, cleaning data, writing SQL queries, and producing summary reports from structured data.
- • Create, populate, or maintain databases for tracking validation activities, test results, or validated systems.
- • Investigate theoretical or conceptual issues, such as the human design considerations of lunar landers or habitats.
- • Analyze complex systems to determine potential for further development, production, interoperability, compatibility, or usefulness in a particular area, such as aviation.
O*NET tasks for this occupation with the most observed Claude usage (Anthropic task data).
What AI can't do here
At 71% human bottleneck protection, the tasks that remain hardest to automate for Manufacturing engineer include: framing the right question, identifying data quality issues, interpreting results in business context, communicating insights to non-technical stakeholders, and making judgment calls on methodology.
Main insulation channels: Deep preparation + Non-routine work — the work-context dimensions behind this occupation's human bottleneck.
Skills to focus on
Sources: Felten AIOE (2021), Anthropic Economic Index (2026), Eloundou GPT Exposure (Science, 2024), ILO GenAI (2025), Pizzinelli et al. bottleneck model. Full methodology.
From exposure to employment
What could change the employment outcome?
AI capability is only the starting point. Adoption, task design, human oversight, demand, mobility and evidence quality determine how exposure may resolve in practice.
Workplace adoption
Leading sectors represented
Some listed industries have MOM sector adoption evidence; observed-sector average 57.5%.
Task structure
Task evidence available
100% weighted task match · 4% effective coverage
Human advantage
Coordination and judgment still matter
At 71% human bottleneck protection, the tasks that remain hardest to automate for Manufacturing engineer include: framing the right question, identifying data quality issues, interpreting results in business context, communicating insights to non-technical stakeholders, and making judgment calls on methodology.
Hiring and demand
3.4% vacancy rate · 2026 Q1
PMET vacancies increased from Q4 and remained below their year-ago level. Hiring stayed net positive but slowed, while retrenchment incidence remained the highest of the three broad occupation groups. In 2025, this broad occupation group had 0.9% time-related underemployment and 6.9% non-permanent employment. Across the published male and female series, CPI-adjusted median income changed -0.8% to +2.3% from 2018 to 2023.
Career transitions
Automation engineer (including robotics engineer)
easy modeled transition; outcomes still depend on skills, wages and openings.
Evidence strength
medium confidence
4 exposure sources · sensitivity 52–70/100 across source-weight sensitivity checks.
These lenses are reported separately and do not alter AI Exposure Rank. Labour-market figures describe a broad official occupation cluster, not this SSOC occupation alone.
Singapore Now
Current labour market conditions and how they affect this role.
Local conditions are under more strain. Vacancies have softened and displacement signals are less forgiving.
Vacancy
3.4%
↓ 2.9% YoY
Hiring
1.2%
vs 0.7% resign
Retrenchment
2.6
per 1,000 · moderate
Re-entry
69.1%
find work in 12mo· +1.4pp
Professionals, Managers, Executives & Technicians · 2026 Q1
Top Industries
Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.
What You Can Do
Manufacturing engineer has some offset potential, but it depends on task redesign holding up in practice and on workers clearing the main switching frictions.Adjacent routes exist, but switching friction is still high.
Published transition support
Related roles you could transition to
Similarity-basedCompare within Professionals
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 57% of occupations · V8 AI Exposure Rank· University Degree
Raw scores
AIOE 1.283 · θ 0.706 · C-AIOE 0.999
Stability
stable · Optimistic 9% · Pessimistic 15%
Score range (best/worst case)
Exposure sensitivity 45–72% · Rank sensitivity 52–70/100 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 2,596 · Median 4,589 · 75th 6,860
Evidence & sources
Data matching
direct · SSOC 21411
SOL 2026: prefix match
Real-world AI usage: -56% vs estimated
Data quality
medium evidence · 4 exposure sources · direct mapping
100% weighted task match · 4% effective coverage
AI overlap by data source
Weights: aioe 24% · anthropic 26% · eloundou 25% · ilo 26%
Conflicting data signals
Tools & offset factors
Common tools (O*NET proxy)
What helps
- A meaningful share of the work can likely be reorganized around AI rather than removed outright.
What could slow it down
- Credential or licensing barriers could make switching harder than the adjacent-role list suggests.
Worker profile & local context
- Vacancy rate is 3.4% and rose by 0.3 points from last quarter.
- Hiring read: recruitment is running above resignation (1.2% vs 0.7%).
- Retrenchment was moderate at 2.6 per 1,000 employees.
- 69.1% of retrenched workers re-entered employment within 12 months.
- Employer pressure is low, based on 1 captured Singapore-relevant company signals through 2025-02-24.
Worker profile
Gender mix
69% male / 31% femalePublished Singapore worker composition for the detailed occupation family 21 Science & Engineering Professionals.
Employment structure
Employee-heavy96% employees, 4% employers or self-employed workers.
Work arrangement
Mostly full-time4% part-time and 96% full-time in 2025.
Age profile
Mid-career heavy14% aged 15 to 29, 62% aged 30 to 49, and 24% aged 50 or older.
Qualification mix
Degree-heavyDegree 81%; Diploma / professional qualification 15%.
Gross wage by sex
Female median 57% lowerPublished June 2024 gross wage medians: male $6,197, female $2,686.
Where this work is concentrated
Top planning areas
Sengkang, Bedok, Tampines19% of workers in this occupation group live in these three planning areas.
Residential concentration
Broadly distributed30% live across the top five planning areas in the 2020 Census.
Commute pattern
Mid-range commutesEstimated average commute 37.5 minutes. 33% 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.
General role profile on a 0–100 scale. These are modelling inputs, not measurements of an individual job or worker.
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 Manufacturing engineer?
Manufacturing engineer has an AI Exposure Rank of 58/100, meaning its work is more exposed to current AI capabilities than approximately 58% of Singapore occupations. The evidence currently points to workflow redesign; this is a relative rank, not a probability of job loss. AI Exposure Rank: 58/100 (Moderate). Median wage: SGD 4,589/month.
What is the AI exposure rank for Manufacturing engineer?
Manufacturing engineer has an AI Exposure Rank of 58/100, rated Moderate. It ranks higher than approximately 58% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Manufacturing engineer?
Manufacturing engineer has modeled transition pathways to related occupations. The strongest adjacent pathway is Commercial airline pilot, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Manufacturing engineer salary compare in the live market?
Manufacturing engineer earns a median gross wage of SGD 4,589/month in the live market (25th-75th percentile: SGD 2,596-6,860). This is near median across all 562 scored occupations, and 29% below group median within Professionals occupations.