Hiring for AI Startups
Dover
May 16, 2025
•
4 min
The market for machine-learning skills is hotter than ever. Graduates with applied‐AI projects on GitHub, mid-career engineers comfortable with MLOps, and researchers who can turn a paper into working code all get peppered with LinkedIn messages daily. The skilled candidates have multiple opportunities to choose from. However, it’s equally challenging for early-stage founders to find these skilled candidates, as they run into five common obstacles while looking for them:
Limited supply meets soaring demand — According to multiple salary surveys, openings labeled AI engineer have more than tripled since 2022, yet the number of professionals with deep model-building experience has grown slowly. Candidates often juggle three or more offers.
Fast-moving skill requirements — The tech stack and tools change weekly. A new model or library, think FlashAttention, or a fresh open-source RAG framework, can become table stakes overnight. You need hires who learn for fun, not because a manager told them to.
Shift to AI-assisted coding — Engineers now alternate between writing prompts and functions, using tools like GitHub Copilot, Cursor, Claude, or Windsurf to co-develop code. Folks who treat these assistants as teammates can outpace peers who ignore them. Screening for that mindset is still new ground for many interview loops.
Brand recognition gap — While a top university grad knows what OpenAI can pay, your five-person startup might be invisible. Every interaction must convey purpose, autonomy, and upside to make their decision easier.
Resource constraints — Most early teams lack dedicated HR. Someone has to keep the hiring operations running and conduct interviews. Ignored and forgotten DMs cost skilled candidates.
“You don’t beat bigger companies by outspending them. You win by giving candidates a story they want to write the next chapter of," A Dover Recruiting Partner.
Since roles in AI and ML have surged in demand post-2022, it’s important to identify your key early hires early on to craft a focused hiring strategy.
1. Product-minded AI Engineer
Early engineering hires touch every layer such as data pipelines, training loops, back-end APIs, and often the front-end. Look for:
Portfolio over title: Check side projects on Hugging Face over job titles.
AI-native workflow: Uses Copilot or Cursor as a first reviewer and iterate faster.
MLOps familiarity: Tools like Weights & Biases to track and ship models.
Ships fast: Works in small batches, shorter feedback loops, and isn’t afraid to cut scope.
2. Data-savvy Product Lead
A founder often covers the product until Series A. As usage grows, bring in someone who can:
Turn messy usage logs into dashboards with SQL or Hex.
Write briefs that speak to both researchers and marketing.
Balance user delight against model speed and cost.
Sketch wireframes when no designer is around.
3. Technical Storyteller for Go-to-Market
Whether the title is Account Executive #1 or Growth Lead, your first GTM hire must translate complex AI concepts into plain outcomes:
Takes a consultative approach, asks about the prospect’s process before jumping into a demo.
Feed objections and feature requests back to engineering the same afternoon.
Experiments with channels: community posts, webinars, or live coding sessions.
Tools are changing how teams build. Encourage early hires to treat AI co-pilots as part of their workflow:
Pair program with Copilot on the boilerplate.
Use Claude for quick code reviews or refactors.
Test prompts in Windsurf to draft unit tests.
Spike ideas in Cursor before committing to a feature spec.
Create a Slack thread where engineers swap screenshots of prompt-and-response wins. Celebrate the weird ones that save a commit. This attitude keeps productivity high and attracts candidates who already have a similar work process.
Fractional Recruiters: A Flexible Way to Add Firepower
Given the ups and downs of startup hiring with tight budgets, a full-time recruiter is not feasible before a headcount of 30. A fractional recruiter, often tenured in startups part-time for such hiring bursts, can get in for 10-20 hours a week and keep the pipeline warm.
Why founders choose the fractional model
Cost control: pay by the hour instead of handing an agency 25% of first-year salary.
Targeted focus: one specialist works your roles rather than a rotating cast in an agency.
Process ownership: they build scorecards, schedule interviews, and coach interviewers so founders can focus on the product.
Dover’s marketplace curates recruiters who know early-stage startup life. In a few clicks, you can meet vetted recruiters with deep AI networks, review their track record, and spin them up inside your Slack. Founders keep visibility through an agency portal that logs every candidate and stage, no more mystery around where a search stands.
Case Study: A Series A ML startup serving top U.S. retailers hired two engineers in just 4 weeks with Dover. Without a full-time talent team, they relied on Dover to handle outreach, screens, and scheduling, saving weeks of effort and cutting candidate drop-off.
Tools That Cut Busywork
A polished process beats a bigger compensation package more often than you might think. Candidates remember clarity and respect during the interview process. Here’s a toolkit that keeps you organized without adding payroll:
Dover ATS: free, unlimited jobs and users. Post once, publish to 70+ job boards. Every applicant lands on one dashboard.
AI resume scoring: The system ranks applicants against your must-have skills, so you start with the top ten instead of the top hundred.
LinkedIn sourcing extension: grab a profile, auto-find an email, send a personalized note, and sync to the ATS in two clicks.
Referral portal: share a link with advisors or investors; track referred candidates end-to-end.
Hiring analytics: see pass-through rates, offer acceptance, and where promising talent drops. Adjust and improve fast.
For founders who love spreadsheets, data exports are available, but most teams stick with the built-in charts because each metric updates in real time.
A Six-Step Playbook for Fast, Fair Hiring
Write a “day 90 success” plan before drafting the job post: Clarify what the hire will own for the first three months. Keep it short and concrete.
Publish everywhere in one click: Your Dover ATS pushes to LinkedIn, Indeed, Wellfound, and similar 70+ niche AI job boards.
Stack interviews in a single week: Candidates stay warm; your team keeps context between calls.
Close with storytelling: Paint the upside of equity, the freedom to ship, and exposure to tech. Share doc links on Git Hub or Loom demos so they can picture the work.
Onboard like you court: First-week wins, shipping a small PR, meeting users, setting morale and retention on the right path.
Common potholes and how to avoid them
Slow feedback loop: ghosting for a week loses senior talent. Block calendar time each day for scorecard updates.
Unrealistic comp bands: use real-time market data inside Dover, or ask your recruiter before quoting equity.
Over-scoped take-homes: four-hour projects scare away strong matches. Aim for ninety minutes.
Measuring Success Beyond Headcount
Conclusion: How Startups Win Top AI Talent
Table of contents
