The only way to tell is to read the papers and judge for yourself, and this is the root of the problem: evaluating candidates based on the number of papers and the impact factor (or some analogous metric) of the venues is much, much easier for committees, than actually reading the papers.
If a panel of respected tenured professors in the field ranked the best works in open access journals each month, or if there was a high profile yearly prize for best open access papers of the year, that could help disaggregate reputational effects from the publication itself.
Bootstrapping this sort of thing is hard. I'm sure Nobels didn't signal as strongly in year one as they do now.
The same for counting quotations, by the way. This may be different in STEM fields, but I'm in the humanities. In the humanities bad papers are sometimes quoted as often as brilliant papers, because everybody wants to correct the authors. I've seen more than one colleague literally make his career by writing a polemic and obviously wrong book (books=often improper peer review) and then sneaking a few apologetic articles in which they half-retracted their claims past the reviewers. What's worse, I know postdocs who are actively looking for such untenable topics that will surely stir up a reaction. We're talking about theses, conjectures or claims that nobody defended previously, because they are so obviously wrong or flawed. I've also met people at the postdoc level who literally replied "Who cares, it's one more publication" when I raised doubts about their main claim in an article and mentioned that it might not 'work' for purely formal reasons (but adding that I'd have to check, to be polite).
I'm sure it's a bit less drastic in CS and the STEM fields, but in the humanities publication counting almost enforces mediocrity and the publication of uninteresting, pointless papers with vastly overlapping topics - and it's completely obvious that it does so.
The situation is particularly frustrating, because the system makes it harder to publish good science. Good science needs larger studies and/or better formal underpinnings, both of which take more time and more effort than duplicating fake papers about fake problems.
Well, enough ranting for today, you get the point.
The onus is on the administrations to get a metric that is gamed by advancing human knowledge, instead of throwing logs at flame wars. That's why they get the big salaries.
In other words, I doubt anyone is explicitly penalized for submitting to PLoS Biology instead of Journal of Neuroscience. However, there is an implicit penalty for not submitting to Nature/Science/Cell.
[As an aside, I really hate the obsession with IF and all these other "quantitative" measures of research quality. They're all incredibly ad-hoc, someone easily game-able, and yet otherwise-smart people take them so seriously.]
Competition for positions in academia has gotten so bad that it doesn't matter. Like Google rejecting perfectly good candidates just because they can, but in Academia even south central Kansastucky state university has enough excellent applicants that arbitrary metrics do more good than harm.
Particularly for early career people, I suspect most of these "quantitative" measurements are basically reflecting where and wtih whom you worked.
They used to have this nice trick where you hired the son of your friend and your friend hired your son. But at least now there are some non-subjective requirements to be met before anyone can get admitted.
Also, the concept of IF being a proxy for rigorous review and work quality is weak at best. Just read a few pages of http://retractionwatch.com/
(Disclaimer: I used to work in this field at biomedcentral.com)
The higher the IF, the more likely it is the researcher trying to do something 'hard' and the more people will scrutinise the results. These two factors alone will lead to more retractions.
Someone publishing something completely uninteresting in a journal no one reads will never be in a position to have to retract their paper, no matter how flawed, because no one will notice and no one will care.
Not to mention the opportunity cost associated with OA fees, which can add up if you're talking about lab startup.