# Postal Service Mail Sorters, Processors, and Processing Machine Operators

> Prepare incoming and outgoing mail for distribution for the United States Postal Service (USPS). Examine, sort, and route mail. Load, operate, and occasionally adjust and repair mail processing, sorting, and canceling machinery. Keep records of shipments, pouches, and sacks, and perform other duties related to mail handling within the postal service. Includes postal service mail sorters and processors employed by USPS contractors.

- **SOC code:** 43-5053.00
- **Canonical URL:** https://singulariki.com/roles/role-43-5053-00
- **Also known as:** Automation Clerk, Distribution Clerk, Mail Handler, Mail Processor, Computer Forwarding System Markup Clerk (CFS Markup Clerk), Flat Sorting Machine Clerk (FSM Clerk), Mail Handler Equipment Operator, Mail Processing Clerk
- **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):
- Direct items according to established routing schemes, using computer-controlled keyboards or voice-recognition equipment.
- Check items to ensure that addresses are legible and correct, that sufficient postage has been paid or the appropriate documentation is attached, and that items are in a suitable condition for processing.
- Clear jams in sorting equipment.
- Bundle, label, and route sorted mail to designated areas, depending on destinations and according to established procedures and deadlines.
- Operate various types of equipment, such as computer scanning equipment, addressographs, mimeographs, optical character readers, and bar-code sorters.
- Move containers of mail, using equipment, such as forklifts and automated "trains".
- Open and label mail containers.
- Load and unload mail trucks, sometimes lifting containers of mail onto equipment that transports items to sorting stations.
- Distribute incoming mail into the correct boxes or pigeonholes.
- Sort odd-sized mail by hand, sort mail that other workers have been unable to sort, and segregate items requiring special handling.
- Rewrap soiled or broken parcels.
- Train new workers.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Near Vision _(ability)_
- Manual Dexterity _(ability)_
- Monitoring _(essential_skill)_
- Written Comprehension _(ability)_
- Information Ordering _(ability)_
- Category Flexibility _(ability)_
- Perceptual Speed _(ability)_
- Multilimb Coordination _(ability)_
- Static Strength _(ability)_
- English Language _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Speaking _(essential_skill)_

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

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft SharePoint _(hot technology)_
- Microsoft Word _(hot technology)_
- SAP software _(hot technology)_
- Teradata Database _(hot technology)_
- Address Management System AMS
- Automated Package Processing System APPS
- Barcode reader software
- Delivery operations information system DOIS
- Delivery Routing System DRS

## AI exposure & outlook

- **AI task-overlap index:** 9th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 11th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 14th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 14th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 64th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -8.4% growth (Declining); 7.8k annual openings; 106.4k → 97.5k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $56,530; 111,930 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/
- **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-43-5053-00_