The suitors problem (aka the 37% rule, an optimal stopping algorithm) doesn't apply to job offers unless you have to either accept or decline the offers on the spot. A better solution would just be set a deadline of date x, tell each job offer you'll have an offer to them by date x, look at all of your options together, and pick the best.
The time to implement the suitors problem or 37% rule is when you have to accept or decline each option as you see it. You know what you've declined, but not what is left. A very micro example is you have 3 playing cards, and you want as high of a value as possible. (I'm also adding in the threshold rule, it significantly improves the 37% rule).
Card 1: if it's not an ace (98th + percentile), don't keep it
Card 2: if it's higher than card 1, or is a king (90th percentile), keep it. Otherwise, leave it.
Card 3: you have to take it if you didn't take card 1 or card 2.
The purpose of the 37% rule isn't necessarily to pick the best option, just to pick a good option, and it's the best algorithm for the specific scenarios that you apply it in, and is significantly (and statistically) more likely to give you a better option than other methods. It can be applied where choices > 2, has a set max number of choices, and all of the choices are randomly ordered.
I would go into it more, but my last long explanation and examples didn't post correctly. For some great reading (even if you don't have a math background!) I highly recommend Algorithms to Live By by Brian Christen and Tom Griffiths.
Fun fact: the 37% comes from the value of 1/e