Medium AI Risk Slow Growth

Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders

SOC Code: 51-9012

Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders carries a 35% AI exposure score (Medium automation risk), with a median annual wage of $49,500 and -4.3% projected employment growth from 2024 to 2034 (BLS), affecting approximately 54,400 workers. Full task breakdown, skills, and employer data are below.

AI Exposure Score
35% Medium

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

Projected Growth
-4.3%
2024–2034 (BLS)
-2,300 jobs
Median Annual Wage
$49,500
BLS May 2024
How wage figures are sourced →

AI Exposure vs Industry Growth

Workforce demand by occupation Sanctioned bespoke signature viz (@signature-viz, KIZ-799) showing occupation-level workforce demand from BLS OEWS data. Pure SVG, no external dependencies.Projected Growth 2024-2034 (BLS)Technology+12.8%Healthcare+10.2%Professional+7.8%Education+5.8%Construction+4.5%Finance+4.6%Logistics+3.2%Government+1.2%Manufacturing-2.1%Retail-3.4%
National AI Exposure
40%
Average across all occupations
Avg Wage Growth
+3.2%
Median annual wage change
High-Risk Roles
127
Occupations with >70% AI exposure

Total occupations tracked

832

Covering all SOC major groups

Data currency

2024

BLS Employment Projections

AI exposure avg

40%

Fleet-wide median across all roles

Methodology confidence 92.0%
Industry standard

Composite score weighing O*NET task data completeness, BLS projection methodology, and cross-validation with employer risk grades.

Employment Projections

54,400
Employment 2024
52,000
Projected 2034
-4.3%
Change (%)
-2,300
Change (jobs)

Occupation Insight

Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders (SOC 51-9012) carries an AI exposure score of 35%, placing it in the Medium automation-risk tier. This score is computed from O*NET Database 30.0 task-level analysis, where each task an occupation performs is evaluated against current generative AI, robotic process automation, and machine-learning capabilities. A score below 40% reflects tasks anchored in physical dexterity, unstructured environments, or high-touch human interaction that current AI cannot reliably replicate.

The economic context matters alongside the risk score. BLS counted approximately 54,400 workers in this occupation in 2024, and projects a -4.3% change through 2034 — a decline that often compounds with high AI exposure to create displacement headwinds. Median annual compensation stands at $49,500, reflecting both skill scarcity and the value employers place on the tasks that remain difficult to automate. Entry typically requires High school diploma or equivalent, plus None of related experience.

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 Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure. Pair the AI exposure score with the BLS employment projection and wage percentiles above for a complete career assessment.

Education & Entry Requirements

Typical Education
High school diploma or equivalent
Work Experience
None
On-the-Job Training
Moderate-term on-the-job training

Top Tasks (O*NET)

  1. 1. Dump, pour, or load specified amounts of refined or unrefined materials into equipment or containers for further processing or storage.
  2. 2. Operate machines to process materials in compliance with applicable safety, energy, or environmental regulations.
  3. 3. Monitor material flow or instruments, such as temperature or pressure gauges, indicators, or meters, to ensure optimal processing conditions.
  4. 4. Turn valves or move controls to admit, drain, separate, filter, clarify, mix, or transfer materials.
  5. 5. Set up or adjust machine controls to regulate conditions such as material flow, temperature, or pressure.
  6. 6. Examine samples to verify qualities such as clarity, cleanliness, consistency, dryness, or texture.
  7. 7. Start agitators, shakers, conveyors, pumps, or centrifuge machines.
  8. 8. Inspect machines or equipment for hazards, operating efficiency, malfunctions, wear, or leaks.
  9. 9. Measure or weigh materials to be refined, mixed, transferred, stored, or otherwise processed.
  10. 10. Test samples to determine viscosity, acidity, specific gravity, or degree of concentration, using test equipment such as viscometers, pH meters, or hydrometers.

Key Skills Required

  • Operations Monitoring
  • Critical Thinking
  • Monitoring
  • Operation and Control
  • Quality Control Analysis
  • Reading Comprehension
  • Active Listening
  • Judgment and Decision Making
  • Time Management
  • Writing

Knowledge Areas

  • Production and Processing
  • Mechanical
  • English Language
  • Public Safety and Security
  • Mathematics
  • Education and Training
  • Chemistry
  • Computers and Electronics
  • Engineering and Technology
  • Customer and Personal Service

Frequently Asked Questions

Will AI replace Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders?

Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders has an AI exposure score of 35%, indicating a medium level of automation risk. The majority of tasks in this role require human judgment, creativity, or physical presence that AI cannot easily replicate.

What is the job outlook for Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders?

According to BLS Employment Projections 2024-2034, Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders is projected to decline by 4.3% over the decade. Current employment stands at approximately 54,400 workers.

What skills are needed for Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders?

Key skills for Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders include Operations Monitoring, Critical Thinking, Monitoring, and others. Typical entry-level education is High school diploma or equivalent.

How much do Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders earn?

The median annual wage for Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders is $49,500, 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 Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders?

The typical entry-level education for Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders is High school diploma or equivalent. Employers generally expect None of related work experience. On-the-job training typically involves Moderate-term on-the-job training. Requirements can vary by employer and specialization.

Which companies employ Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders?

Separating, filtering, clarifying, precipitating, and still machine setters, operators, and tenders 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

1.8
out of 5.0

Medium automation risk based on 10 analyzed tasks. Most tasks require human judgment and are resistant to automation.

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).

Related

Data sourced from official public datasets. See our methodology for details. Retrieved and formatted by PlainWorkforce Editorial