A $500,000 compensation ceiling will get attention. If you are hiring for a startup, the useful question is which job that number belongs to.

In a check of OpenAI's careers listings on September 19, its Machine Learning Engineer, Distributed Data Systems role in robotics listed $380,000 to $500,000 plus equity. The work involves infrastructure for multimodal training and evaluation, including distributed data pipelines.

A separate Software Engineer, Distributed Data Systems opening listed $230,000 to $385,000 plus equity. Similar names. Different ranges.

The Operations Program Manager opening for robotics data acquisition listed $177,000 to $251,000 plus equity. That person would help run data-collection facilities and improve the reliability of daily operations.

These are advertised ranges for particular openings. They are not offers, a survey of the market, or evidence that every robotics engineer should expect the same package. The pages checked did not establish when these jobs were first posted.

Founders need a more specific comparison

I would start with the work you need done before looking at another company's maximum number. Are you hiring someone to build distributed training infrastructure? Run a collection operation? Own the software that keeps a robot working in the field?

Those searches can lead to different people. A broad title makes it harder to explain why a particular candidate is right for your team, and harder to decide whether your budget fits the search.

Write down what the person must accomplish in the first six months. Then decide which experience is essential and which experience someone can learn. Use relevant compensation ranges as one input alongside your stage, location, scope and equity terms.

You also need a clear answer when a candidate asks why they should choose you. Ownership, access to the founder and an interesting technical problem can matter. They do not remove the need for a credible package. Talk about all of it early enough that neither side spends weeks pursuing an offer that cannot work.

Candidates should compare the full job

Before anchoring on the top of a posted range, ask how the employer determines level and where your experience fits. Compare responsibilities, location requirements and equity separately. A maximum listed on a page is not a promise about your offer.

Then make your relevant experience easy to assess. If your strength is data infrastructure, explain a system you owned, the scale it handled and a failure you resolved. If your strength is operations, show how you improved quality or reliability.

The useful lesson from these listings is how much specificity matters. Be clear about the problem you solve. Expect the employer to be equally clear about the job and the package.