Three job postings, one ladder: FDE pay from one year of experience to eight

Palantir is hiring Forward Deployed Software Engineers with a requirement of just over one year of experience after graduation, at an estimated base salary of $135,000–200,000 a year. At the same time, OpenAI has posted a vacancy for a Manager, Forward Deployed Engineering at $345,000, asking for more than eight years of experience, including at least two years managing FDEs.

Between those two postings lie seven years of a career — and a ladder anyone can read on a public job board.

That is rare for a profession this new, where pay and progression are usually hard to benchmark. Set the Palantir and OpenAI postings alongside public salary datasets and three things stand out: the entry point is low, a management tier already exists, and from the middle rung up, total compensation pulls away from base salary because of equity.

For a developer considering a move into forward deployed engineering — whether in Vietnam, where this piece first appeared, or anywhere else — the ladder answers two practical questions: which level to aim for, and which numbers in salary reports can be compared with which.

The entry point is lower than you think

The most striking detail in the Palantir posting is not the salary but the experience line: more than one year of relevant work after graduation. One year.

The estimated base salary for the role is $135,000–200,000, total compensation may include Restricted Stock Units, and the job involves travelling to customer sites up to 25% of the time.

In other words, at Palantir an FDE does not need to be senior already. If you have two or three years of work behind you, you already clear the bar on years. The real barrier, if there is one, lies in the word “relevant” — and that is a question for your CV, not your tenure.

The middle rung: OpenAI’s floor sits above Palantir’s ceiling

One step up, OpenAI’s posting for a Platform Engineer in Forward Deployed Engineering in New York asks for more than five years of experience as a software or ML engineer and lists a range of $230,000–385,000 a year plus equity. OpenAI’s $230,000 floor is already above Palantir’s $200,000 ceiling.

The $155,000 gap between floor and ceiling for the same title also tells you something. The requirement is only a five-year minimum, so someone who has just reached it and someone well past it can apply to the same posting — and a range that wide leaves room to pay those two profiles differently.

Add equity that has not been quantified, and this is the rung at which total compensation begins to separate clearly from base salary.

The manager tier is real

OpenAI’s Manager, Forward Deployed Engineering posting lists a single figure: $345,000. It requires more than eight years of technical or technical-delivery experience, including more than two years managing FDEs, and the hire will lead an FDE team.

The posting matters less for its number than for what it shows: the route to management runs inside the profession. The requirement is experience managing FDEs, the job is leading an FDE team; promotion does not mean switching to a different title. At least at one frontier lab, FDE has its own management tier.

The $345,000 figure also needs careful reading. The posting describes it as “compensation”, a single number, without splitting base and equity as the Platform Engineer posting does. So do not set it directly against the individual contributor’s $230,000–385,000 range to decide who is paid more; the two postings use different yardsticks.

Three medians, three different measures

Here, three medians barely line up.

An analysis of 135 job postings by a recruiting firm puts the median FDE base salary in 2026 at $190,000 across all locations, with an interquartile range of $160,000–220,000. Levels.fyi, where engineers self-report, gives a median total compensation of $216,250.

Perspective AI’s salary report, compiled from Levels.fyi, Glassdoor, job postings, Blind and self-reported data, puts median total compensation for mid-level FDEs at $385,000, staff level at $610,000, and principal at frontier labs above $1.2m.

Figure	Source	What it measures	How to read it
$190,000	135 job postings, analysed by a recruiting firm	Base salary stated in postings; interquartile range $160,000–220,000	Consistent with the Palantir and OpenAI postings above, but it is recruiter marketing content
$216,250	Levels.fyi, self-reported	Total compensation, US market	Figures self-reported by people in the job
$385,000	Perspective AI, compiled from Levels.fyi, Glassdoor, Blind, job postings, self-reports	Median mid-level total compensation, US market	Vendor report that reuses Levels.fyi data; read as a trend

The spread is not a contradiction, because each number measures something different. $190,000 is base salary in job postings. $216,250 is total compensation self-reported by real people. $385,000 is mid-level total compensation according to a vendor report, and that report itself says equity at frontier labs is what pulls the top end up.

Nor are the three datasets independent: Perspective AI’s report uses Levels.fyi data as one of its inputs. And both the $190,000 source and the $385,000 source are content from companies selling recruiting services or software.

So none of the three is a negotiating benchmark. The benchmark is the posting from the company you are applying to. The one merit of the $190,000 figure is that it matches the postings above — in the upper half of Palantir’s range and below OpenAI’s floor — so it can be used to cross-check base salary.

Perspective AI’s report also assigns Anthropic’s Applied AI Engineer a range from $300,000 at L3 mid-level to $1.2m at L6 principal. Those L3–L6 levels are the report’s own classification, not a ladder Anthropic has published.

But they reinforce the same message: from mid-level to principal, total compensation in that report rises fourfold, and by the report’s own account the top end comes from equity, not base salary.

Which numbers apply outside the US?

Every figure above is for the US market. Perspective AI’s report states plainly that its data covers only the US, and that pay outside the US is only 50–70% of US levels.

That 50–70% belongs to that report alone; it says nothing about Vietnam or any specific remote role. The only lesson to draw is this: do not carry a US pay range unchanged into a negotiation for a role outside the US, and ask the company directly which band it places the role in.

So what should you do with the ladder? The first piece of advice is to aim at the right rung.

With two to four years of experience, you clear Palantir’s 1+ year bar but have not reached OpenAI’s 5+ year mark; from five years up, both kinds of posting are open. Whichever group you are in, do not skip an “entry” role just because your years exceed the requirement: its pay range still spans $65,000.

The second is to read postings in three columns: years, pay range, and structure beyond salary. The three postings above show that the third column changes with the level: RSUs that “may be included” at Palantir, equity written into the range at OpenAI, and a single “compensation” figure at the manager tier.

And when writing your CV, anchor it to the requirements in the Palantir posting itself: “relevant” post-graduate experience and willingness to work at customer sites up to 25% of the time.

The most convincing way to prove that word “relevant” is a single line describing a time you worked directly with a customer or end user, took their requirements and delivered the result yourself — worth more than a list of technologies.

The ladder is already written on the job boards, with concrete years and dollar amounts. What remains is deciding which rung to step on first.