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Key statistics

The identity block — every fact is canon, none is generated.

1 of 8

Track

Individual contributor

Family rungs priced

P1 · P2 · P3 · P4 · P5 · P6

51-0000

manufacturing

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Entry-level professional who operates computer-controlled machining equipment under guidance to produce parts meeting basic quality standards. At the Entry-Level Professional (P1) level, the role owns own tasks within a defined component, routine problems with known solutions, and drives own deliverables. None — building the craft.

What this level does

Adjusts wear offsets in the machine control to bring finished dimensions onto their target under close direction of a senior machinist
Deburrs finished parts and measures them with gauges and hand instruments, flagging any part that falls outside stated tolerance
Loads stock and cutters, monitors cutter wear during running jobs, and reports tool changes needed to the setter
Follows setup sheets and verbal instructions step by step, learning the sequence of a basic milling or turning job

Knowledge applied: Applies basic shop knowledge — reading a gauge, adjusting a wear offset, deburring — under detailed instruction; learning how a machining job is sequenced.
Complexity & problem solving: Handles routine tasks with standard answers; escalates anything outside the setup sheet to a senior machinist.
Collaboration & interaction: Works alongside and takes direction from setters and senior machinists within a stable shop team.
Typical experience: 0–1 years; entry, apprentice, or trainee with no prior machining experience required.

Skills at this level

Machine operation and setup: — Sets up and troubleshoots mills, lathes, drill presses, grinders, hones, and CNC centers, applying good manufacturing practices and selecting tooling and machining resources appropriate to the part being made.
CNC programming (G-code): — Generates and modifies programmatic G-code instructions that the machine interprets to cut components, applying knowledge of speeds and feeds, machine tooling, work-holding, and material behavior.
Blueprint and GD&T reading: — Reads blueprints and Geometric Dimensioning and Tolerancing callouts to interpret datums, position, profile, and dimensional specifications for a part before machining and inspection.
Precision measurement and inspection: — Reads instrumentation and gauges to measure machined dimensions within a .001 tolerance, performing first-article and in-process inspections to confirm parts meet the print.
Machining mathematics: — Uses trigonometry and shop math to follow technical drawings and calculate part dimensions, tool positions, machine speeds, and feed rates for precision work.
Machining process optimization: — Revises speeds, feeds, toolpaths, sequencing, and fixturing in collaboration with engineers to reduce cycle time and scrap and improve the capability of an entire machining process.
Fault diagnosis and troubleshooting: — Diagnoses machining problems by isolating root cause across program, machine, tooling, fixture, and material, using logic and reasoning to weigh alternative fixes.
Tooling and equipment maintenance: — Maintains machine tools and manages tooling inventory, monitoring cutter wear and equipment condition to keep parts within tolerance and machines running reliably.
Fabrication and part repair: — Fabricates, reconstructs, and repairs mechanical parts and tooling from detailed blueprints, including welding and manual finishing where the routing is not fully defined.
Failure prediction through analysis: — Predicts where tolerance stacks, thermal growth, tool deflection, and fixturing will cause a part to fail before it is cut, so high-consequence jobs run correctly the first time.
Shop mentorship and training: — Trains junior and senior machinists on setup, programming, and inspection, and establishes proven standard methods that others in the shop adopt.

Model-authored from 21 retrieved sources, then adversarially reviewed by an independent judge panel and held to a minimum-evidence floor before publication. Distinct from corpus-extracted content, which is labelled as such.

Pay across the ladder

Machine Shop — median with , by . Real canon data.

Pay figures are modeled estimates from our market equation — level-differentiated, with honest-wide bands. They refresh as new leveling evidence lands; treat per-role dollars as directional.

Demand — is the field growing, or just turning over?

SOC 51-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

-22.1%Machinists · United States

383,470 → 298,790 jobs

29,500 openings / year, split into:

replacement / exits 29,500

Growth — new jobs (0%)Exits — backfill (100%)

This occupation is projected to contract — every opening is replacing someone leaving, not net new demand.

Projected growth 2024–34 contracting: 0.0%
Growth share of hiring: 0%
Exit / retention pressure high: 100%
Openings / year: 29,500

Two quantities, kept apart: observed = employment growth differenced from two BLS OEWS vintages (what already happened); projected = BLS Employment Projections 2024–34, openings decomposed into growth + exits (growth derived from projected employment change). Neither is a pay figure.

Where this role sits — growth vs. pay

level P1 held

Demand axis is projected growth (not raw openings — that just measures churn). A curated, de-duplicated set of recognizable occupations held at this level; this role is highlighted.

Where it pays — by metro

OEWS metro median

San Jose-Sunnyvale-Santa Clara, CA$106k

San Francisco-Oakland-Fremont, CA$95k

Washington-Arlington-Alexandria, DC-VA-MD-WV$88k

Boulder, CO$87k

Seattle-Tacoma-Bellevue, WA$87k

Boston-Cambridge-Newton, MA-NH$83k

Lexington Park, MD$81k

Trenton-Princeton, NJ$81k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $64k; the demand-cube initiative will add growth-by-metro.

What changes across the ladder

Level-distinction canon isn't available for the adjacent levels of this profile yet.

Level differentiators

What separates P1 (P1 — Entry-Level Professional) from the levels around it — this level only. Every word is real canon for this profile.

Scope: Own tasks within a defined component
Autonomy: Close supervision; work reviewed frequently
Complexity: Routine problems with known solutions
Impact: Own deliverables
Decision rights: Few independent decisions; escalates the rest
Leadership: None — building the craft

Full level-by-level matrix (side by side across the ladder) lives on the Ladder drill-down — not here.

Promotion

The next rung from P1 is P2.

Performance criteria for this profile render in the assessment kit →

People also watch

Nearest coordinate neighbors — same , same level. Each row's (confident / review / no match) comes from its — computed, not curated.

Profile	Key	Median	Δ structural
VP of Machine Shop Operations	jf_machine_shop::E1	$174,847	confident0.06
Machine Shop Supervisor	jf_machine_shop::M1	$88,342	confident0.06
Machine Operator I	jf_machine_shop::S1	$38,510	confident0.06
CNC Machinist	jf_machine_shop::P2	$74,658	confident0.06

Capability cluster

O*NET 51-4041

Core to this role. Click any element with an ⓘ for its O*NET definition. A job-specific capability model (SkillFrame) will sharpen this and add worked examples.

The Job Swatch

P1

jf_machine_shop::P1

Associate CNC Machinist

Machine Shop

The swatch is JobFrame's core primitive: a hueset deterministically by the role's superfunction, the profileKey as its color code, the level ladder, and a content fingerprint. Two swatches that look alike are alike.

Because every role carries computable coordinates, similarity, leveling, and pricing become measurable instead of arguable— the whole point of "Pantone for jobs."

Structural

structural@1

Where it sits— position in the work taxonomy + level (weighted Gower, level > function > focus).

Track: P
Superfunction: manufacturing
Function: manufacturing_expansion
Family: machine_shop
Universal level: 1 / 8

Content

content@2

What it's about— a fingerprint over the role's tasks & capabilities.

Two roles can sit far apart structurally yet be close in content — that gap is itself a signal.

3-state matching

How any two swatches compare — a real distance in coordinate space, resolved to a calibrated verdict (never a fake "94% match").

Chemistry, Manufacturing & Controls (CMC) · P1review0.17

Lean Manufacturing · P1review0.17

Maintenance · P1review0.17

Manufacturing Engineering · P1review0.17

Pilot Plant Operations · P1review0.17

Production Test · P1review0.17

Editions — frozen & versioned

structural@1live

Released 2026-06-17

content@2live

Released 2026-07-28

pay@2live

Released 2026-07-02

semantic@1live

Released 2026-07-02

Coordinates belong to a frozen, versioned edition — a coordinate computed today still means the same thing next quarter. Re-fitting issues a new edition, so comparisons are never silently redefined.

How to read this profile

Every number here is traceable — to a named source, a stated method, and an as-of date — and each value is labeled by how we know it: observed from data, computed by a model, projected forward, or governed canon. Nothing is a black box, and nothing is invented to fill a gap.

Where every number comes from

What	Source & method	Edition	State
Median pay + range	JobFrame pay equation (function base × family multiplier × level × focus differential)	pay@2	computed
Pay across the ladder	Same equation applied at each universal level	pay@2	computed
Demand — growth & openings	BLS Employment Projections; openings decomposed into growth + exits	EP 2024–34	projected
Pay by metro	BLS OEWS metropolitan medians	OEWS 2024	observed
Level distinctions	JobFrame universal-level canon (scope · autonomy · complexity · impact · decision rights · leadership)	canon	canon
Capability cluster	O*NET Knowledge / Skills / Abilities	O*NET 51-4041	computed
Coordinates	JobFrame coordinate system — structural (weighted Gower) + content fingerprint	structural@1 · content@2 · pay@2 · semantic@1	computed
Related roles	Nearest neighbors in the structural coordinate space	structural@1	computed

The principles behind the numbers

Ranges, not false precision

Pay is P10 · median · P90, never a single misleading number.

Three honest states, never a fake %

Role matches resolve to confident / review / no-match — a calibrated verdict, not a made-up percentage.

Provenance on every value

Each number carries whether it's observed, computed, projected, or canon — so you always know how much to lean on it.

Gaps shown as gaps

When a figure isn't available at the grain you need, we say so — we don't fabricate a number to fill the cell.

Frozen, versioned editions

Pay and coordinate editions are pinned, so a comparison you make today still means the same thing next quarter.

Privacy by min-N

Any aggregate over people is suppressed below a minimum count, so no individual is identifiable.

Freshness

As of2026-07-30Editioncanon-v2@2.1.0-completeSourcecanon-v2 cell + salary-benchmark (model)