Welcome to the third installment of our biannual TalentEdge newsletter. We are often asked by clients and candidates what we are seeing in the market. This is an attempt to share what we observe, specifically what has changed over the last six months (it’s a lot this year).
Our worldview is admittedly very narrow and as a result, most of these observations will be subjective without broader data to support sweeping market trends. Context on who we are to help frame these observations:
A high-signal recruiting partner for venture-backed, early-stage companies.
We specialize in complex, technical industries where execution quality, domain fluency, and access to elite passive talent materially change outcomes.
Founders come to us when the role is high-stakes, the talent is passive, and getting it wrong would be expensive.
In everything we do, we can lean too far towards complete transparency and these observations are no different.
“The more AI we use, somehow the more people we have to hire.” - Nvidia CEO Jensen Huang
Forward Deployed Engineer is quickly becoming one of the hottest titles at early stage companies. Half engineer, half account manager, comfortable enough with customers to sit in the room and communicate while still being the builder. These roles have been around at companies like Palantir and Anduril for a few years and are now taking off at AI startups as many AI products don’t work off the shelf at the enterprise level. Selling an AI product into a large company in 2026 is also selling an integration project - the model itself is the easy part, making it actually work inside someone’s messy real-world systems is the hard part.
Candidate sentiment - Engineers are split. Some love the variety and exposure to different problem spaces, others see it as a detour from the deep technical work they’d rather be doing full time.
Startup impact - Showing up most frequently within defense tech, AI, and regulated enterprise verticals that are aggressively scaling implementation. Not a role all early stage companies need yet, but a strong signal of where a company is in its lifecycle.
Long term projection - Doesn’t look like a fad. As AI products need more hands-on deployment, expect FDE to become a permanent lane, not a placeholder title.
Companies are moving towards assessing a candidate’s ability to solve actual problems and their ability to communicate their thinking, rather than algorithm riddles that had nothing to do with the job. Of our current clients, only ~10% are still using Leetcode-style coding interviews. Most of these are AI-assistant friendly and this is oftentimes its own competency now. How you are setting up and working with your tool of choice is critical.
Candidate sentiment - Relief mixed with anxiety. Years of leetcode prep suddenly could feel wasted, and there’s no clear playbook yet for practicing a live conversation instead.
Startup impact - Faster loops and fewer false negatives on strong engineers who never tested well on puzzles. Results are also less standardized, which makes comparing candidates harder.
Long term projection - Early stage is leading this shift. Expect it to take longer to reach big tech, where scale still rewards a more standardized process.
What matters now is whether you’ve shipped using AI tools, and whether you can keep up as the tools change every few months. Any “good” startup is using a modern tech stack that any “good” software engineer has either worked with or can learn in a few weeks.
Candidate sentiment - Deep specialists in one stack are feeling less differentiated, even senior engineers with strong pedigree. Surprisingly, most people seem fine with it.
Startup impact - Sourcing funnels are widening fast. Companies aren’t gatekeeping on exact stack match anymore, which opens up who they’re even willing to consider.
Long term projection - The gap between AI-fluent and AI-resistant engineers is widening quickly, and it is already starting to show up in comp. That said, what is starting to matter most is how effectively engineers are using AI and LLMs.
Job boards are flooded with AI-polished applications that all sound the same. Referrals and warm intros are becoming the only reliable way to find people, because a hiring manager can’t trust a cold resume.
Candidate sentiment - Tons of candidates feel invisible. A good resume doesn’t stand out when everything reads the same. (Work with a good recruiter to get you a foot in the door!!)
Startup impact - Recruiting partners and referral networks are becoming the real filter, since inbound can’t be trusted at face value.
Long term projection - Expect more companies to shift toward live or verified assessments as the fix - which ties directly back to the algorithm interview shift above.
For candidates -
Stop grinding algorithm prep. Put that time into practicing how you talk through your thinking on real problems and how you set up and leverage your AI toolkit - that’s what’s actually being evaluated.
Show your AI-native work, don’t just claim it. A public GitHub repo with real projects built using current AI tools does more for you than a resume line - have one ready to walk through.
Don’t cold apply. Recruiters and referrals cut through the noise AI-written applications created.
For startups -
Redesign your technical rounds around real work, not algorithm puzzles. Live problem solving and talking through actual code tell you more than a leetcode score ever did.
Screen for AI fluency and versatility over narrow stack pedigree. The best candidates are the ones who’ve shipped with current AI tools and can sit in front of a customer, not just the ones with 5 years in one framework.
Build a referral engine and revisit comp bands quarterly. Inbound applications can’t be trusted the way they used to be, and comp bands set 12 months ago are already behind the market.
We are excited for what the rest of the year brings and are looking forward to helping our clients compete with the top firms. We would love to hear from you if you have any specific questions or need a partner to hire in such a competitive market.
TE Team
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