# Microsoft, Starbucks and Qualcomm jobs, ten rows at a time: querying Eightfold for machine learning engineer jobs

> Source: <https://promptcube3.com/en/threads/10126/>
> Published: 2026-10-27 02:23:42+00:00

# Microsoft, Starbucks and Qualcomm jobs, ten rows at a time: querying Eightfold for machine learning engineer jobs

Here is the direct answer: Microsoft, Starbucks, Qualcomm and others run their careers sites on Eightfold, and the listings load from one public search endpoint. For machine learning engineer jobs, querying that endpoint with server-side filters beats paging through listings by hand.

*Illustrative photo · [Vitaly Gariev](https://www.pexels.com/photo/creativeteamcollaborationatmodernofficetable39190569/) / Pexels*

The request pattern is fixed: the host takes the employer's own domain, so qualcomm.eightfold.ai pairs with domain=qualcomm.com, starbucks.eightfold.ai with domain=starbucks.com, and apply.careers.microsoft.com with domain=microsoft.com. Each row carries a name, locations, department, a posted timestamp, a work-location option and a position link. Descriptions are not in the rows; they need a second position_details call.

Paging is the first reality to plan around: asking for 100 rows still returns 10. Qualcomm had 2,072 open roles on the day measured, which takes 208 requests to read fully, while Starbucks had 21,693, which takes 2,170. Read the first answer's count before deciding to page through everything, and do not page what you do not need.

Rate limits are the second reality: Microsoft's host answered 429 after a short run of requests, and later after only one or two requests even spaced ten seconds apart. Qualcomm was far more tolerant but still refused now and then, recovering a minute or two later. The rule is simple: back off on a refusal instead of retrying at once, and ask a strict host for less from the start.

The cheapest move is letting the server filter: the query and location parameters are applied on Eightfold's side. Narrowing Qualcomm's 2,072 roles to San Diego left 517, cutting the work from 208 requests to 52. Filter first, page second.

One more trap: the older endpoint that took 100 rows a page now answers 403 and is closed; the current search endpoint is the one to use. And remember that search rows carry no description by design, so budget for the second call whenever you need the actual posting text.

A practical routine follows from all of this: read the count first, filter server-side, page only what you need, and back off on refusals. These figures were measured on 2026-10-26 and each host's tolerance can change, so re-check the current robots file and the live responses before relying on them.

[Next Building with Meta models: an ai engineering roadmap chapter on Llama 3.1, 3.2, and newer variants →](https://promptcube3.com/en/threads/10121/)

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