66
/100

SOC 51-9012

Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders

High RiskFrey/Osborne: 88.0%

Risk Score

โš ๏ธ

66/100

High Risk

US Employment

๐Ÿ‘ฅ

54,200

Total workers

Median Wage

๐Ÿ’ฐ

$50K

$36K โ€“ $76K

Projected Growth

๐Ÿ“ˆ

-4.3%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

48/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders face a risk score of 66/100 โ€” 22 points above the national average of 44. With only 48/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $50K ($3K above the national median). The 3 recommended career transitions all maintain competitive wages while reducing automation exposure. Explore transition paths โ†’

๐Ÿ” AI Impact Analysis

With a risk score of 66/100, Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders faces moderate automation pressure. While tasks like smart factory scheduling and production optimization are increasingly handled by AI, the role retains significant human elements. The 54,200 workers in this occupation should focus on strengthening skills in troubleshooting complex equipment malfunctions and coordinating workflow across diverse production teams to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders?

Read our full analysis with verdict, risk factors, safe tasks, and career transition paths โ†’

โš ๏ธ Top Risk Factors

1

Smart factory scheduling and production optimization

2

Cobots handling repetitive material handling tasks

3

Automated CNC programming and machine operation

4

AI quality inspection via computer vision systems

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Troubleshooting complex equipment malfunctions

โœ“

Coordinating workflow across diverse production teams

โœ“

Handling non-standard materials and configurations

๐Ÿ“Š Task Automation Breakdown

Based on O*NET task analysis and GenAI exposure scoring. Shows the estimated proportion of this occupation's core tasks that are automatable by current AI, augmented by AI tools, or require essential human skills.

๐Ÿ“‹ O*NET Task Profile

โ€ข

Dump, pour, or load specified amounts of refined or unrefined materials into equipment or containers for further processing or storage.

โ€ข

Operate machines to process materials in compliance with applicable safety, energy, or environmental regulations.

โ€ข

Monitor material flow or instruments, such as temperature or pressure gauges, indicators, or meters, to ensure optimal processing conditions.

โ€ข

Turn valves or move controls to admit, drain, separate, filter, clarify, mix, or transfer materials.

โ€ข

Set up or adjust machine controls to regulate conditions such as material flow, temperature, or pressure.

๐Ÿ’ป Technology Skills

โ€ข

Electronic mail software

โ€ข

Spreadsheet software

โ€ข

Office suite software

โ€ข

Presentation software

โ€ข

Word processing software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

Mechanical

โ€ข

English Language

โ€ข

Public Safety and Security

โ€ข

Mathematics

๐Ÿ“Š vs National Average

Median Wage$50K
+$3K

National avg: $46K

Risk Score66/100
+22

National avg: 44/100

GenAI Exposure48/100
+10

National avg: 38/100

Projected Growth-4.3%
-8.0%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K57%
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K67%
Fabric and Apparel Patternmakers33$68K81%