How to Hire AI Engineers at Startups (September 2025)
Dover
August 22, 2025
•
3 mins
The demand for skilled AI engineers has skyrocketed, with 21% AI career growth. For startups, hiring the right AI talent can determine whether you successfully integrate AI features or fall behind competitors.
Getting this hire right accelerates your entire product development cycle. A strong AI engineer can prototype new features in weeks rather than months, validate technical approaches before you invest heavily, and build scalable systems that grow with your user base.
The competitive advantage is real. Startups with experienced AI engineers can iterate faster on LLM features, make data-driven product decisions, and often punch above their weight against larger competitors. They can also help you avoid costly technical debt that comes from implementing AI solutions without proper expertise.
The reality is that there are so many challenges with AI hiring that most startups aren't prepared for. The talent pool is limited, expectations are high, and the technical evaluation process requires specialized knowledge.
Our recruiting platform combines this human expertise with AI-powered tools that simplify the entire process, from sourcing to final offer negotiations.
Start by clearly defining the role and responsibilities of your future AI engineer, as vague descriptions often attract the wrong candidates and waste valuable time. Be specific about the technologies they'll work with, expected outcomes, and the types of problems they'll solve.
Separate generalist AI engineers who can handle multiple tasks from specialists focused on areas like computer vision or natural language processing. Most early-stage startups benefit from generalists who can adapt as your AI needs evolve.
Consider your current technical infrastructure and data maturity. If you're still collecting and cleaning basic user data, you might need someone with strong data engineering skills. If you already have clean datasets, focus on candidates with model development and deployment experience.
The biggest mistake startups make is writing AI engineer job descriptions that sound like they're hiring for Google or OpenAI. Your role should reflect startup realities and growth opportunities.
Think about the business impact you expect from this hire. Are you building AI features for users, optimizing internal processes, or looking into new product directions? Each scenario requires different skills and experience levels.
Our guide on choosing the right recruiter includes frameworks for clarifying these requirements before you start the hiring process.
Finding top AI engineers requires using professional networks, attending AI conferences and meetups, partnering with universities, and using specialized job boards like AI Jobs, Kaggle, or Data Science Central. Traditional job boards often miss the passive candidates who represent the best talent.
Dover's AI resume scoring system ranks applicants against your must-have skills, so you start with the top ten instead of the top hundred. This dramatically reduces the time spent on initial screening while making sure you don't miss qualified candidates.
Our sourcing tools include a LinkedIn extension that finds candidate email contacts and allows personalized outreach in two clicks. This is particularly valuable for AI engineers who are often passive candidates with multiple opportunities.
The key is building a multi-channel sourcing strategy. Academic conferences and research communities often yield candidates with cutting-edge knowledge. Open source contributions on GitHub can reveal practical skills that don't show up on traditional resumes.
Employee referrals work exceptionally well for AI roles because the technical community is relatively small and well-connected. Dover's referral system makes it easy to tap into your team's networks and track referred candidates through the entire process.
We've compiled the top engineering job boards with specific recommendations for AI and machine learning roles.
Consider specialized AI recruiting firms that understand the technical nuances and have built relationships with top talent.
Conduct Technical Assessments and Interviews
Technical assessments should involve coding challenges and AI-specific tasks that assess a candidate's practical abilities. Present candidates with real-world AI problems and have them explain their reasoning for developing, deploying, and scaling specific algorithms.
We recommend structuring your technical interviews in multiple stages: (1) Initial technical screen focusing on fundamentals, (2) Take-home project that mirrors actual work, (3) On-site technical discussion with your engineering team, (4) System design discussion for senior candidates.
The take-home project should be realistic but respectful of candidates' time. A 2-4 hour project that involves data analysis, model building, or algorithm implementation gives you insight into their approach and code quality.
During interviews, focus on their problem solving process rather than just correct answers. How do they approach new problems? How do they handle incomplete information? Can they explain complex concepts clearly?
Dover's fractional recruiters can help design appropriate technical evaluations based on your specific needs and industry best practices. They've seen what works across hundreds of AI hiring processes.
Our guide on preventing interview cheating includes specific strategies for AI and machine learning assessments.
Check Cultural Fit and Startup Readiness
While a top university grad knows what OpenAI can pay, your five-person startup might be invisible, so every interaction must convey purpose, autonomy, and upside to make their decision easier. AI engineers joining startups need comfort with ambiguity and willingness to wear multiple hats.
Look for candidates who thrive in environments where they can have major impact on product direction. Ask about times they've worked with limited resources or unclear requirements. How did they handle competing demands?
Assess their learning agility. AI is evolving rapidly, and startup AI engineers need to stay current with new techniques while focusing on business outcomes. Look for evidence of continuous learning and adaptation.
Consider their collaboration style. In startups, AI engineers work closely with product managers, designers, and other engineers. They need to translate between technical possibilities and business requirements.
The best startup AI engineers are excited about building something from scratch rather than optimizing existing systems. They should be energized by the opportunity to shape your AI strategy rather than frustrated by the lack of set processes.
Our research on company values research shows that cultural alignment greatly affects retention and performance.
Negotiate Offers and Close Hires
Once you've identified the right candidate, be prepared to negotiate salary, benefits, and other perks to secure top talent. Candidates need to understand the opportunities behind early-stage companies when you're asking an AI engineer to leave a $500,000 salary at a big tech firm.
Startups must compete on equity, growth opportunities, and meaningful work rather than cash compensation. Highlight the potential upside of joining early, the scope of impact they'll have, and the learning opportunities that come with building AI systems from scratch.
Structure your equity offer thoughtfully. Explain the potential value, provide context about the company's growth path, and be transparent about the risks. Many AI engineers understand startup equity but appreciate clear communication about expectations.
Consider non-traditional benefits that appeal to AI engineers: conference attendance budgets, research time, publication opportunities, or access to cutting-edge hardware and tools.
Dover's fractional recruiters understand current market rates for AI talent and can guide your offer strategy. They know what competing offers look like and how to position your startup opportunity effectively.
Our guide on startup compensation guide includes specific frameworks for equity and compensation decisions.
Onboard and Retain Your Engineers
Frequently Asked Questions
Final thoughts on building your AI engineering team
Table of contents
