The tools job postings name most in October 2026: Python, AWS, Excel, and Claude
Every skills article tells you what employers "want". It is more useful to count what they actually write. Level extracts the tools named in each live posting, which makes it possible to rank them. Here is what the ranking looked like on 16 October 2026, what is surprising in it, and how to use it without over-reading it.
The top of the list
Across 140,530 live postings, the most frequently named tools were:
| Rank | Tool | Postings naming it |
|---|---|---|
| 1 | Python | 8,602 |
| 2 | AWS | 5,409 |
| 3 | Excel | 3,784 |
| 4 | SQL | 3,057 |
| 5 | Kubernetes | 2,722 |
| 6 | Claude | 2,674 |
| 7 | C | 2,572 |
| 8 | Java | 2,500 |
| 9 | PyTorch | 2,481 |
| 10 | Salesforce | 2,390 |
Further down: Microsoft Office (2,149), Copilot (1,954), OpenAI (1,772), Databricks (1,721), Cursor (1,597) and Docker (1,433). The ranking comes from Level's market statistics for the same day.
Three things that stand out
Excel beats Kubernetes. Spreadsheets sit at number three, above container orchestration. The reason is that these counts cover every live posting on the board, and most postings are not AI roles at all. A tool ranking that includes finance, operations and sales roles will look different from one built only from machine learning jobs.
An AI assistant is in the top ten. Claude is named in 2,674 postings, ahead of PyTorch (2,481) and Salesforce (2,390). Copilot (1,954), OpenAI (1,772) and Cursor (1,597) follow. Employers increasingly list the assistant a person is expected to work with, not only the platform a model runs on. On the site's tool index the same pattern continues with Claude Code (1,078), LangChain (884), ChatGPT (852) and LangGraph (603).
The same tool appears under several spellings. The index lists Microsoft Office (2,112 at the time of that page render) and Ms Office (885) separately, and Excel appears alongside Microsoft Excel and Ms Excel. Tool names come from the posting text, so spelling variants are counted apart. If you are building your own analysis, merge them before you compare.
How to use a tool ranking
- Rank by your target role, not the global list. Python is number one overall, but a sales operations job and a model training job will not share a skill profile. Filter first, count second.
- Separate "must" from "mentioned". A count says how many postings name a tool. It does not say whether the tool is a requirement or a nice-to-have, and a posting that lists twenty tools is not asking for twenty things.
- Watch the assistant tools as a signal of Level 2. A role that names Copilot, Claude or Cursor next to ordinary duties is often an ordinary job that expects AI-assisted work. That is the Level 2 pattern: the requirement comes from the role, not from an AI company.
- Look for frameworks to find Level 4. PyTorch (2,481 mentions), TensorFlow, JAX and vLLM tend to appear where building or serving models is the work.
- Treat counts as weather, not climate. Level re-reads employer systems every six hours, so a figure on the day you read it will differ from the one printed here.
A quick exercise
Take a role you want. Open ten live postings for it and write down every tool they name. Sort the list by frequency. The top five are your baseline. Then check whether any of the five is an AI tool: if two or three are, the role is probably moving toward Level 2 or 3, and your portfolio should show you using them on a real task, not just listing them.
Method
- Data: counts of live postings that name each tool, from Level's market statistics on 16 October 2026 (140,530 live postings); lower-ranked figures from the public tool index on the same date.
- Extraction: tools come from the posting text, so spelling variants are counted separately.
- Limits: a mention is not a requirement, and the counts include every category, not only AI roles.
Level publishes the full, live list on its AI tools used in job openings page.
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