The Sales Engineer role is genuinely difficult to hire for. It sits at an unusual intersection — technical enough to earn credibility with engineers and architects, commercially savvy enough to support complex enterprise sales, and interpersonally skilled enough to build trust across a buying committee that may span multiple organizations. Finding people who can do all three well is hard. Designing an interview process that reliably identifies them turns out to be harder.
Most hiring processes for SE roles don't do it well. They borrow from adjacent disciplines — the technical screen from software engineering, the culture fit panel from sales, the live demo from whatever someone remembered from their own interview. The result is a process that tests for a collection of loosely related things and produces inconsistent results.
The most common failure modes
Over-indexing on technical depth. Technical screens designed for software engineers tend to evaluate the wrong dimension for SE work. SEs don't need to write production code — they need to understand systems well enough to have credible conversations with people who do. A candidate who aces a LeetCode-style technical assessment may struggle in a live customer environment where the answers aren't well-defined and the audience is more interested in outcomes than implementation details.
The "cold demo" problem. Asking a candidate to demo a product they've never seen in a 30-minute screen is a test of how well someone performs under artificial stress with incomplete information. That's a real skill — but it's not the primary one. In practice, SEs work on accounts over months, develop deep context, and prepare carefully. The cold demo doesn't reflect that reality.
Relying on impression over evidence. "Did we like them?" is a natural question after an interview, but it's a weak signal for SE performance. The most effective SEs aren't always the most immediately impressive in a room. Some of the best customer-facing technical people are quieter, more methodical, and more effective over time than they appear in a 45-minute panel.
What to interview for instead
Judgment, not knowledge. Knowledge can be learned. Judgment — knowing when to be technical vs. when to stay at the business level, when to push vs. when to listen, when to say "I don't know" vs. when to defer — is harder to develop and more predictive of SE success. Scenario-based questions that have no single right answer reveal judgment in a way that factual questions don't.
Discovery skills. The ability to ask good questions is arguably the most underrated SE skill. A strong SE can walk into a conversation with limited information and surface what actually matters to the customer — the real problems beneath the stated requirements, the political dynamics shaping the decision, the success metrics that will determine whether a deal closes. Give candidates a business scenario and watch how they explore it.
How they handle being wrong. Deliberately introducing a technical error or ambiguity in an interview reveals something important. Does the candidate catch it? If they don't, do they recover gracefully when it's pointed out? The ability to say "you're right, let me think about that differently" in front of a customer — without losing credibility — is a skill that separates good SEs from great ones.
Written communication. SEs spend a significant portion of their time writing — follow-up emails, POV proposals, success criteria documents, executive briefings. Almost no interview process tests this directly. A simple written exercise — ask the candidate to draft a follow-up email from a scenario you describe — surfaces skills that a panel interview misses entirely.
The AI dimension
One thing that's changed recently: the SE hiring profile has shifted. Candidates who understand how to use AI tools effectively — who can build a pre-call brief in three minutes instead of thirty, who can draft a POV proposal and then improve it rather than starting from scratch — bring meaningfully more capacity than those who don't.
That's worth evaluating directly. Not by testing which AI tools they know — those change constantly — but by understanding how they think about their own workflow and where they look for leverage. The SEs who are most effective with AI are the ones who have a clear sense of what they're trying to accomplish and are pragmatic about the tools they use to get there.
What better looks like
After rethinking how our team approaches SE evaluation, a few things stand out as having the most impact: structured rubrics that separate signals from impressions, scenario-based questions that reveal judgment rather than recall, and a written exercise that tests communication skills directly. Not a complete overhaul — mostly a deliberate shift in what we're looking for and how we're looking for it.
The ramp time and retention numbers improve when the hiring process is better calibrated to the actual job. That's the outcome worth optimizing for — not a process that feels rigorous, but one that actually predicts success in the role.