I read all 308 open jobs at xAI: 54% are in its data center team, and most of them are in Memphis

A job board is a company's plan written as headcount. On 31 October 2026 I pulled every open posting from xAI's public Greenhouse board and counted them by the department label xAI itself assigns.

I expected model researchers. I found construction superintendents.

Disclosure: I built the data tool used here. Details and a way to reproduce this are at the end.

The headline number

xAI had 308 open jobs. 167 of them (54%) are filed under the department "Data Center".

The next biggest departments are far smaller:

Only 7 jobs sit in the department called "Model". Post-Training (3) and Vision (2) are even smaller, and those five are all "AI Tutor" postings. The board is not hiring a research army. It is hiring the people who build and run the building the research runs in.

One small oddity: the board lives at job-boards.greenhouse.io/xai, but the company name it reports on each posting is "SpaceXSI". I simply report what the board says.

What "Data Center" means in practice

I searched the 167 Data Center titles for a few keywords (a title can match more than one):

Some real titles from the board, verbatim:

• Construction Superintendent, Power Generation - Memphis
• Director, Power Plant Operations - Memphis
• Asset Manager, Energy Storage - Memphis
• Chiller Technician - Memphis
• Certified Weld Inspector - Memphis
• Construction Rigger - Memphis
• Fire Protection Engineer - Memphis
• HVAC Supervisor, Chilled Water Systems - Memphis
• Construction Superintendent, Electrical - Night Shift (Memphis)

Power generation, battery storage, chilled water and night shifts. That is the vocabulary of a utility or a large construction site, not of a software company.

Where the jobs are

Almost all Data Center roles point to one area:

That is 165 of 167 in the Memphis and Southaven area, right on the Tennessee and Mississippi border. Across the whole board, the top locations were Southaven/Memphis (174 postings) and Palo Alto (74).

The 22 "AI Tutor" jobs

The board lists 22 "AI Tutor" roles (12 in "Human Data", the rest in STEM, Post-Training, Vision and Legal & Compliance). All 22 are marked temporary, and many are language-specific (examples: "AI Tutor, Uzbek - Temporary", "AI Tutor, Oromo - Temporary", "AI Tutor, Latvian - Temporary"). These are people who write and grade training data in specific languages. It is a small but telling line item: frontier models still need humans for the long tail of languages.

How this compares to two software-first AI companies

Same day, same tool, title-based counts (a rougher method than department labels, so treat these as approximate):

*Sales, solutions, success, partnerships, marketing, business development, forward-deployed and similar.

xAI posts the fewest customer-facing roles of the four and by far the most physical-infrastructure roles. Cursor sits at the other extreme: more customer-facing titles than engineering titles.

What I take from it

1. Compute is now a construction business. When more than half of an AI lab's open roles are about power, cooling, fiber and concrete, the bottleneck is physical, not algorithmic.
2. Location follows power. The jobs cluster where the site and its power generation are, not where AI talent traditionally lives.
3. Job boards are an early signal. Hiring for a power plant director and night-shift construction superintendents tells you about build-out plans months before a press release does.

This is one snapshot. Job boards change daily, and a posting is a plan, not a hire.

How to reproduce this

I pulled the board with my Career Site Jobs API on Apify. It reads public Greenhouse, Lever, Ashby, Workday and similar boards and returns one clean JSON row per job (title, department, location, salary range if published, URL).

Input I used:

{
  "companies": ["greenhouse:xai", "ashby:openai", "ashby:perplexity", "ashby:cursor"],
  "includeDescription": false,
  "maxJobsPerCompany": 2000
}

Pricing is $0.002 per job returned, so the 308 xAI jobs cost about $0.62. Counting by department is then a few lines of Python:

from collections import Counter
jobs = [j for j in rows if j["company"] == "SpaceXSI"]
print(Counter(j["department"] for j in jobs).most_common())

If you track a company's build-out, run it on a schedule and diff the results: new departments and new locations show up there first.