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Pile Driver Operators

Occupation · SOC 47-2072.00

Operate pile drivers mounted on skids, barges, crawler treads, or locomotive cranes to drive pilings for retaining walls, bulkheads, and foundations of structures such as buildings, bridges, and piers.

Also called: Pile Driver · Pile Driver Operator · Pile Driving Operator · Diesel Pile Hammer Operator · Driving Inspector · Driving Operator · Hoisting Pile Driving Engineer · Hydraulic Pile Hammer Operator · Hydraulic Press-In Operator · Nozzle Operator · Pile Driver Engineer · Pile Driving Inspector

Job family: Construction and Extraction Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-47-2072-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

1st-percentile task overlap — yet about 300 openings a year (+4.3% projected, BLS) . What exposure means →

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Low 10th -1.2
LLM task exposure, γ (OpenAI / Eloundou) Low 3rd 0.0
AI assistant applicability (Microsoft) Low 0th 0.0

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.0), and including AI-powered software (γ 0.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Historical automation estimate (2013)

A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.

Frey–Osborne probability 0.8 · 66th percentile among occupations · Moderate

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook About average · +4.3% by 2034
Projected annual openings 300
Employment 2024 → 2034 3,200 → 3,300

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

13% mean task exposure (2025)
11th percentile of 427 placed occupations
+1 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Earthmoving and Related Plant Operators · 8342 13% Not exposed

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Tasks

All 5 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Transferable skills

Operation and Control 4.1
Operations Monitoring 4.0
Equipment Maintenance 3.3
Troubleshooting 3.1
Coordination 3.0
Quality Control Analysis 2.9

Knowledge

Building and Construction 4.1
Mechanical 3.6
Mathematics 3.6
Transportation 3.5
Engineering and Technology 3.4
Public Safety and Security 3.4
Design 3.1
Production and Processing 3.0
Administration and Management 2.9

Abilities

Control Precision 4.1
Multilimb Coordination 4.0
Depth Perception 4.0
Reaction Time 3.9
Manual Dexterity 3.6
Problem Sensitivity 3.5
Rate Control 3.5
Response Orientation 3.4
Selective Attention 3.3
Arm-Hand Steadiness 3.3
Deductive Reasoning 3.1
Visualization 3.1
Far Vision 3.1
Speech Recognition 3.1
Oral Comprehension 3.0
Oral Expression 3.0
Inductive Reasoning 3.0
Information Ordering 3.0
Spatial Orientation 3.0
Finger Dexterity 3.0
Near Vision 3.0
Auditory Attention 3.0

Essential skills

Monitoring 3.3
Active Listening 3.0
Critical Thinking 2.9

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Microsoft Excel Spreadsheet software Hot technology
Email software Electronic mail software
Global positioning system GPS software Mobile location based services software
GRL Engineers Wave Equation Analysis Program GRLWEAP Analytical or scientific software
Pile Dynamics Case Pile Wave Analysis Program CAPWAP Analytical or scientific software
Pile Dynamics Pile Driving Analyzer PDA Analytical or scientific software

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

Outdoors, Exposed to All Weather Conditions 5.0
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 5.0
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.8
Health and Safety of Other Workers 4.7
Face-to-Face Discussions with Individuals and Within Teams 4.6
Work With or Contribute to a Work Group or Team 4.6
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.5
Exposed to Contaminants 4.5
Contact With Others 4.5
Exposed to Hazardous Equipment 4.4
Coordinate or Lead Others in Accomplishing Work Activities 4.3
Impact of Decisions on Co-workers or Company Results 4.2
Importance of Being Exact or Accurate 4.2
Consequence of Error 4.2
Frequency of Decision Making 4.2
Work Outcomes and Results of Other Workers 4.0
Exposed to Very Hot or Cold Temperatures 4.0
Freedom to Make Decisions 4.0
Physical Proximity 3.9
In an Open Vehicle or Operating Equipment 3.8
Spend Time Making Repetitive Motions 3.7
Exposed to Whole Body Vibration 3.7
Time Pressure 3.6
Determine Tasks, Priorities and Goals 3.5
Telephone Conversations 3.5
Exposed to Minor Burns, Cuts, Bites, or Stings 3.5
Pace Determined by Speed of Equipment 3.3
Exposed to Extremely Bright or Inadequate Lighting Conditions 3.3
Spend Time Standing 3.3
Spend Time Bending or Twisting Your Body 3.2
Level of Competition 3.2
Exposed to Cramped Work Space, Awkward Positions 3.2
In an Enclosed Vehicle or Operate Enclosed Equipment 3.1
Spend Time Sitting 3.1
Importance of Repeating Same Tasks 3.1
Exposed to High Places 3.0
Wear Specialized Protective or Safety Equipment such as Breathing Apparatus, Safety Harness, Full Protection Suits, or Radiation Protection 3.0
Conflict Situations 3.0
Spend Time Walking or Running 2.9
Spend Time Kneeling, Crouching, Stooping, or Crawling 2.7

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
High school diploma or equivalent · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

What to study: Transportation and Materials Moving . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

High School Diploma 52.6%
Post-Secondary Certificate 20.9%
Less than a High School Diploma 18.6%
Some College Courses 7.9%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Realistic 7.0
Conventional 3.6
Investigative 1.9
Enterprising 1.1

Interest areas

Physical/Manual Labor 6.1
Transportation/Machine Operation 5.8
Construction/Woodwork 2.8
Engineering 2.7
Mechanics/Electronics 2.5
Mathematics/Statistics 1.4
Nature/Outdoors 1.4

Work styles

Dependability 2.3
Cautiousness 2.1
Attention to Detail 1.6
Stress Tolerance 1.5
Perseverance 1.2

Wages & employment

U.S. · annual wages (BLS OEWS)

$47k10th$55k25th$71kMedian$104k75th$122k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
3k20243k2034 (proj.)+4.3% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $46,690
25th percentile $54,750
Median (50th) $70,510
75th percentile $103,960
90th percentile $121,990
People employed 3,040

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Industry Workers National median pay
Construction · Sector 2,890 $71,760
Poured Concrete Foundation and Structure Contractors · National industry 280 $105,100
Power and Communication Line and Related Structures Construction · National industry 50 $56,310

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Industry Concentration Workers
Poured Concrete Foundation and Structure Contractors · National industry 54.89× 280
Construction · Sector 18.05× 2,890

Part of the Construction career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Pile Driver Operators sits at the 1st percentile of AI task-overlap and the 60th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Pile Driver Operators Paving, Surfacing, and Tamping Equipment Operators Industrial Truck and Tractor Operators Earth Drillers, Except Oil and Gas Mobile Heavy Equipment Mechanics, Except Engines AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Pile Driver Operators — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Adjacent roles

Occupations O*NET rates as related — the nearby moves on the map.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 11th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Pile Driver Operators show 1st-percentile AI task overlap — and about 300 annual U.S. openings

  • Pile Driver Operators rank in the 1st percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 300 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be about average (+4.3%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $70,510, across about 3,040 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Pile Driver Operators show 1st-percentile AI task overlap — and about 300 annual U.S. openings

• Pile Driver Operators rank in the 1st percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 300 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be about average (+4.3%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $70,510, across about 3,040 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Pile Driver Operators". https://singulariki.com/roles/role-47-2072-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Pile Driver Operators." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-47-2072-00

APA

Singulariki. (2026). Pile Driver Operators. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-47-2072-00

BibTeX 
@misc{singulariki-role-47-2072-00,
  title  = {Pile Driver Operators},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-47-2072-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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