High AI Risk Average

Engineers, all other

SOC Code: 17-2199

Engineers, all other carries a 50% AI exposure score (High automation risk), with a median annual wage of $117,750 and +2.1% projected employment growth from 2024 to 2034 (BLS), affecting approximately 158,800 workers. Full task breakdown, skills, and employer data are below.

AI Exposure Score
50% High
Above-average exposure

Proportion of tasks susceptible to AI automation (O*NET analysis)

Projected Growth
+2.1%
Typical growth
2024–2034 (BLS)
+3,300 jobs
Median Annual Wage
$117,750
Top 10% pay
BLS May 2024
How wage figures are sourced →
The verdict

At 50% AI exposure, Engineers, all other sits 13 points above the 37.2% average across 832 U.S. occupations - more exposed than 87% of them. Most of its core tasks still require human judgment.

50%
AI exposure
Top 13%
most exposed
+2.1%
Job growth 2024–34
$117,750
Median wage
C-

Career outlook score

52/100

Engineers, all other, weighted across 3 of 3 tracked dimensions

A weighted composite of median wage, projected growth, and AI-automation security (inverted exposure), each benchmarked against every other tracked occupation. It describes this occupation's standing across those three dimensions, not a guarantee of future outcomes.

  • Median wage A+ $117,750

    Annual median wage, percentile rank vs all tracked occupations

  • Projected growth D +2.1%

    Projected employment change, 2024-2034, BLS Employment Projections

  • AI exposure (inverted) F 50% exposure

    Lower O*NET task-automability scores lower risk; this dimension scores security, not exposure

Employment vs AI exposure

Home health and personal care aides is #1 of 832 occupations by employment (4.3M, AI exposure rank #392 at 35%) vs. Bookkeeping, accounting, and auditing clerks #1 by AI task exposure (95%, employment rank #17 at 1.6M).

4.3M
Home health and personal care aides employment (#1)
#392
Home health and personal care aides AI rank
95%
Bookkeeping, accounting, and auditing clerks AI exposure (#1)
#17
Bookkeeping, accounting, and auditing clerks employment rank

Automation-risk inventory

Exclusive cut of 832 occupations by PlainWorkforce automation-risk band. Orthogonal to the continuous AI-exposure distribution chart below and to this occupation's single risk badge, this is the corpus band mix, with Engineers, all other in the High band.

Credential × growth standing

Bachelor's band with mid-pack BLS pace

Engineers, all other shares the Bachelor's gate with 178 peer occupations and lands in Average (378 of 832). Neither the rarest nor the most crowded growth shelf - use wage and AI desks on this page for the differentiator.

Where Engineers, all other sits among all occupations

AI-exposure score distribution across 832 U.S. occupations (O*NET task analysis). This occupation is marked in rose.

Where Engineers, all other sits by AI exposure

AI-exposure score distribution across U.S. occupations (O*NET task analysis)

50% Higher than most higher than 87% of 832 occupations

Occupations, banded by AI-exposure score

Each bar is a band; taller bars hold more occupations. The dashed line + filled bar mark this entry. Hover or tap any bar for its full count and share, and where it sits relative to this entry.

Source O*NET task data (U.S. Department of Labor) · BLS 2024–2034

Source: O*NET task data (U.S. Department of Labor); AI-exposure scores are PlainWorkforce's analysis. As of BLS 2024–2034.

Engineers, all other vs. its occupational neighbors

AI exposure (horizontal) vs projected 2024–2034 growth (vertical) for occupations in the same SOC group. Engineers, all other is marked in rose; the top-left corner is the most future-proof.

Future-proofGrowing but exposedStable, lower-AIMost at risk25%36%47%58%69%0%2.9%5.8%8.7%11.6%AI exposure (%) →Projected growth (%) →Civil engineers, AI exposure (%) →: 27% · Projected growth (%) →: 5%Civil engineersIndustrial engineers, AI exposure (%) →: 67% · Projected growth (%) →: 11%Industrial engineersMechanical engineers, AI exposure (%) →: 35% · Projected growth (%) →: 9.1%Mechanical engineersElectrical engineers, AI exposure (%) →: 39% · Projected growth (%) →: 7.2%Electrical engineersEngineers, AI exposure (%) →: 50% · Projected growth (%) →: 2.1%EngineersArchitects, AI exposure (%) →: 39% · Projected growth (%) →: 3.9%ArchitectsArchitectural, AI exposure (%) →: 55% · Projected growth (%) →: 4.1%ArchitecturalElectronics engineer, AI exposure (%) →: 48% · Projected growth (%) →: 6.2%Electronics engineerElectrical, AI exposure (%) →: 46% · Projected growth (%) →: 0.6%ElectricalComputer hardware en, AI exposure (%) →: 48% · Projected growth (%) →: 7.3%Computer hardware enIndustrial engineeri, AI exposure (%) →: 39% · Projected growth (%) →: 1.7%Industrial engineeri

Source: BLS Employment Projections 2024–2034 (growth) and O*NET (AI-exposure analysis by PlainWorkforce).

Employment Projections

158,800
Employment 2024
162,100
Projected 2034
+2.1%
Change (%)
+3,300
Change (jobs)

Where This Score Comes From

Engineers, all other's High automation-risk tier is computed from O*NET Database 30.0 task-level analysis, where each documented task the occupation performs is evaluated against current generative AI, robotic process automation, and machine-learning capabilities. A score in the 40–70% range indicates meaningful automation pressure on specific task categories, but the role as a whole still requires human judgment for coordination, exception handling, or client interaction.

The employment, wage, and education figures above come from a second, separate federal source: BLS Employment Projections 2024–2034, matched to the O*NET task data by Standard Occupational Classification (SOC 17-2199) code, with the wage figure cross-validated against the BLS Occupational Employment and Wage Statistics (OEWS) May 2024 survey. BLS figures are presented exactly as published, with no adjustment on our part; the SOC-code match is what lets Engineers, all other be compared directly against every other tracked occupation on the same basis, not just roles that happen to share a job title.

For career planners, this profile should be read alongside the task, skill, and knowledge breakdowns below and the list of employers whose workforce composition includes Engineers, all other. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure.

Education & Entry Requirements

Typical Education
Bachelor's degree
Work Experience
None
On-the-Job Training
None

Top Tasks (O*NET)

  1. 1. Identify and recommend energy savings strategies to achieve more energy-efficient operation.
  2. 2. Conduct engineering site audits to collect structural, electrical, and related site information for use in the design of residential or commercial solar power systems.
  3. 3. Create plans for solar energy system development, monitoring, and evaluation activities.
  4. 4. Design or coordinate design of photovoltaic (PV) or solar thermal systems, including system components, for residential and commercial buildings.
  5. 5. Conduct energy audits to evaluate energy use and to identify conservation and cost reduction measures.
  6. 6. Provide technical direction or support to installation teams during installation, start-up, testing, system commissioning, or performance monitoring.
  7. 7. Provide scientific or technical guidance or expertise to scientists, engineers, technologists, technicians, or others, using knowledge of chemical, analytical, or biological processes as applied to micro and nanoscale systems.
  8. 8. Supervise technologists or technicians engaged in nanotechnology research or production.
  9. 9. Create mechanical design documents for parts, assemblies, or finished products.
  10. 10. Create electrical single-line diagrams, panel schedules, or connection diagrams for solar electric systems, using computer-aided design (CAD) software.

Key Skills Required

  • Reading Comprehension
  • Critical Thinking
  • Writing
  • Speaking
  • Complex Problem Solving
  • Active Listening
  • Mathematics
  • Judgment and Decision Making
  • Science
  • Active Learning

Knowledge Areas

  • Engineering and Technology
  • Design
  • Building and Construction
  • Mathematics
  • Mechanical
  • Computers and Electronics
  • Physics
  • Administration and Management
  • English Language
  • Customer and Personal Service

Frequently Asked Questions

Will AI replace Engineers, all other?

Engineers, all other has an AI exposure score of 50%, indicating a high level of automation risk. Some tasks in this role can be augmented or partially automated by AI, but core responsibilities require human judgment.

What is the job outlook for Engineers, all other?

According to BLS Employment Projections 2024-2034, Engineers, all other is projected to grow by 2.1% over the decade. Current employment stands at approximately 158,800 workers.

What skills are needed for Engineers, all other?

Key skills for Engineers, all other include Reading Comprehension, Critical Thinking, Writing, and others. Typical entry-level education is Bachelor's degree.

How much do Engineers, all other earn?

The median annual wage for Engineers, all other is $117,750, according to BLS Occupational Employment and Wage Statistics (May 2024). Actual earnings vary by location, experience, industry, and employer. The BLS publishes detailed wage percentiles by region in its Occupational Employment and Wage Statistics program.

What education is required for Engineers, all other?

The typical entry-level education for Engineers, all other is Bachelor's degree. Employers generally expect None of related work experience. On-the-job training typically involves None. Requirements can vary by employer and specialization.

Which companies employ Engineers, all other?

Engineers, all other roles exist across many industries and employers. Workforce composition is estimated from BLS industry-occupation employment distributions matched to SEC-registered public companies.

AI Exposure Rating

2.5
out of 5.0

High automation risk based on 10 analyzed tasks. A moderate share of tasks may be augmented by AI tools.

Nationwide occupations with similar workforce profiles

Two data-derived peer sets for Engineers, all other: AI-exposure neighbors and BLS 2034 growth neighbors (distinct comparison neighborhoods, cross-occupation).

Similar AI exposure score

Nearest O*NET task-automability scores nationwide (50% here).

Similar projected growth (2024–2034)

Nearest BLS employment change rates nationwide (+2.1% here).

Peers are nearest-neighbor matches on published BLS + O*NET metrics among occupations with ≥10,000 workers in 2024; corpus ranks above sort the full tracked set.

What to do with this

Use Engineers, all other's numbers above to compare, not just read in isolation.

AI exposure scores estimate task automatability from O*NET data; they are not a certainty of job loss, and BLS growth projections are estimates, not guarantees.

Data sources: Bureau of Labor Statistics Employment Projections 2024–2034 and O*NET Database 30.0. Employment figures are rounded. Wage data from BLS Occupational Employment Statistics (OES).

Every figure on PlainWorkforce is rendered directly from BLS/O*NET source data, no number is typed in by an editor. See our editorial standards & corrections policy, the methodology behind these figures, or report a data error. Data current as of BLS Employment Projections 2024-2034.

Sources: BLS Employment Projections 2024-2034, O*NET Database 30.0.