The forward deployed engineer is the hottest job in AI right now. FDE job postings grew 800% between January and September 2025 (Fast Company, 2025). OpenAI's FDE org grew from 2 engineers to over 10 in a single year (Hashnode, 2026). By early 2026, FDE teams are standard at OpenAI, Anthropic, Cohere, Databricks, Ramp, Rippling, and Adobe (Gigged.AI, 2026).
And companies are still interviewing FDEs with LeetCode.
What Does a Forward Deployed Engineer Actually Do?
Praveen Kumar Verma calls the FDE "the Special Ops of tech: 50% Architect, 30% SWE, 20% Strategist." That breakdown is more accurate than most job descriptions.
An FDE sits with a customer, diagnoses their actual problem (not the one they described in the ticket), scopes what is buildable in a sprint, and ships a working solution in the customer's environment. Not yours. Theirs.
The role requires reading unfamiliar codebases under deadline pressure, making technical tradeoffs with incomplete information, navigating organizational politics, and communicating all of it to non-technical stakeholders. 47% of FDE roles explicitly require customer-facing skills (Bloomberry, 2025). Yet almost no interview process tests for any of this.
The FDE interview tests the 20% of the job (writing code) and ignores the 80% (scoping, deploying, navigating client politics, handling ambiguity).
Why Does LeetCode Select Against the Best FDE Candidates?
A classic graph algorithm question favors engineers who grind problems at home. The best FDE candidates have been in client environments, debugging strange infrastructure at 11pm, talking a CTO off a ledge. Those people do not have time to grind LeetCode.
The interview selects against itself.
Palantir figured this out years ago. Their "Decomposition" round gives candidates a vague, real-world problem with no defined scope. They evaluate whether the candidate clarifies before coding, identifies subproblem boundaries, handles edge cases from first principles, and communicates tradeoffs as they go. That is close to what FDE work looks like. Almost no other company runs anything similar.
What Should an FDE Evaluation Actually Test?
If you are hiring FDEs and your interview loop looks like your backend engineer loop, you are filtering for the wrong signal. Here is what matters.
Start with problem scoping under ambiguity. Give the candidate a deliberately vague brief. A real client would never hand over clean requirements. Watch whether they ask clarifying questions or just start coding. The best FDE candidates spend the first 20 minutes understanding the problem, not solving it.
Then test whether they can build in an unfamiliar codebase. FDEs do not get to work in their own repo. They drop into a customer's environment, often poorly documented, and ship something that works. Hand them a codebase they have never seen. Give them AI tools. Score how they navigate.
You also need to evaluate client communication under technical pressure. Can this person explain a tradeoff to a non-technical stakeholder while the system is half-built? Can they say "no" to a feature request without losing the relationship? A case study or async scenario works better than a 45-minute coding round for this.
Finally, reward iteration speed over perfection. FDEs ship v1 in days, not weeks. They get feedback from the client and adjust. Your evaluation should favor candidates who validate assumptions early over candidates who architect a flawless system they never test.
How Do You Run This at Scale?
The biggest objection to purpose-built FDE evaluation is time. Palantir can afford a multi-round, customized interview loop. Most companies hiring their first two FDEs cannot.
This is where async evaluation closes the gap. Give the candidate a real problem in a real IDE with AI tools available. Let them work for a few hours instead of performing in a 45-minute window. Score how they scoped the problem, how they validated their output, whether they navigated ambiguity or just brute-forced it. Fairground's AI Coding Screener captures the process, not just the output. Start free. 100 credits. No credit card. No sales call.
The companies that design FDE-specific evaluation now will stop losing their best candidates to Palantir's interview process. Everyone else will keep wondering why their FDE hires quit in six months.
Related: What Skills Should You Actually Test in AI-Ready Interviews?
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