# Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic

> Set up, operate, or tend drilling machines to drill, bore, ream, mill, or countersink metal or plastic work pieces.

- **SOC code:** 51-4032.00
- **Canonical URL:** https://singulariki.com/roles/role-51-4032-00
- **Also known as:** CNC Machinist (Computer Numerical Control Machinist), Drill Operator, Drill Setup Operator, Machine Operator, Bore Mill Operator, CNC Drilling Operator (Computer Numerical Control Drilling Operator), Drill Press Operator, Punch Operator
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Verify conformance of machined work to specifications, using measuring instruments, such as calipers, micrometers, or fixed or telescoping gauges.
- Study machining instructions, job orders, or blueprints to determine dimensional or finish specifications, sequences of operations, setups, or tooling requirements.
- Move machine controls to lower tools to workpieces and to engage automatic feeds.
- Verify that workpiece reference lines are parallel to the axis of table rotation, using dial indicators mounted in spindles.
- Establish zero reference points on workpieces, such as at the intersections of two edges or over hole locations.
- Change worn cutting tools, using wrenches.
- Select and set cutting speeds, feed rates, depths of cuts, and cutting tools, according to machining instructions or knowledge of metal properties.
- Position and secure workpieces on tables, using bolts, jigs, clamps, shims, or other holding devices.
- Observe drilling or boring machine operations to detect any problems.
- Lift workpieces onto work tables either manually or with hoists or direct crane operators to lift and position workpieces.
- Turn valves and direct flow of coolants or cutting oil over cutting areas.
- Install tools in spindles.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Mathematics _(knowledge)_
- Production and Processing _(knowledge)_
- Mechanical _(knowledge)_
- English Language _(knowledge)_
- Operations Monitoring _(transferable_skill)_
- Engineering and Technology _(knowledge)_
- Control Precision _(ability)_
- Arm-Hand Steadiness _(ability)_
- Near Vision _(ability)_
- Education and Training _(knowledge)_
- Design _(knowledge)_
- Problem Sensitivity _(ability)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- English Language _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Visualization _(Specialized Skill)_
- Reading Comprehension _(Common Skill)_
- Time Management _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Word _(hot technology)_
- SAP software _(hot technology)_
- Automated inventory software
- Computerized numerical control CNC software

## AI exposure & outlook

- **AI task-overlap index:** 18th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 27th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 8th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 29th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 86th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -19.6% growth (Declining); 0.4k annual openings; 5.3k → 4.3k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $46,630; 5,310 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-51-4032-00_