If you are an early stage SaaS founder, chances are you know you need AI in your product. What is far less clear is who you should actually hire to build it. Job titles are confusing, expectations are inflated, and many founders end up hiring the wrong role at the wrong time.
In this episode of The SaaS Jobs Podcast, I spoke with Jay Odeka, Director at WunderTalent, about what founders consistently get wrong when hiring engineering and AI talent. Jay works closely with SaaS and AI startups, helping them fix broken hiring processes and build technical teams that actually deliver.
You can watch the full episode here (or on Spotify / Apple Podcasts):
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Below is a breakdown of the most important lessons from our discussion.
Jay’s Background: Hiring for SaaS and AI at Early Stage
Jay has spent years working with SaaS and AI companies across engineering, AI and go to market hiring. His focus is not just filling roles, but helping founders understand what they really need at each stage of growth. Much of his work involves correcting mismatches between job descriptions, company stage and the actual problems teams are trying to solve.
This perspective gives him a practical, commercial view of AI hiring, grounded in delivery rather than hype.
The Biggest Misunderstanding About AI Hiring
One of the most common mistakes founders make is confusing building AI with building a product that uses AI. These are very different problems that require very different hires.
Many early stage SaaS companies are not building novel AI models. They are integrating existing AI tools into a product. In these cases, hiring a PhD level machine learning engineer is often unnecessary. What teams usually need is a strong software engineer who understands APIs, Python, scalable systems and how to ship AI powered features.
Clarity on this distinction alone can save founders months of wasted hiring effort.
Why AI Job Titles Cause So Much Confusion
Terms like AI engineer, ML engineer and data scientist are frequently used interchangeably, but they describe very different skill sets. Jay recommends that founders focus less on titles and more on outcomes.
The key question to ask is not what role do we need, but what problem does this person need to solve right now. Once that is clear, the right skill set often becomes obvious.
Hiring Too Senior, Too Early
Another recurring issue is hiring leadership before there is a team to lead. Early stage companies often jump straight to VP or Head of Engineering roles when what they actually need is a senior individual contributor who can build, ship and set direction.
Leadership roles make sense once there is complexity to manage. Before that point, hands on experience delivers far more value.
Unicorn Job Descriptions and Why They Fail
Many job descriptions try to combine multiple roles into one hire. Engineering, AI, architecture, customer facing work and strategy are all bundled together, often with unrealistic salary expectations.
These unicorn roles rarely exist in the market. When they do, they are typically already employed at top tier companies. Clear, focused roles aligned to the company’s stage consistently outperform over engineered job specs.
What Strong AI and Engineering Candidates Actually Look Like
Jay emphasises that the strongest candidates talk about impact, not just technology. They can explain what they built, why they built it and what changed as a result.
Instead of leading with accuracy metrics or research outputs, strong candidates describe business outcomes such as reduced support tickets, faster workflows or improved customer experience. This commercial awareness is a strong signal, especially in early stage environments.
Designing a Hiring Process That Works
The best hiring processes are simple, relevant and fast. Jay recommends starting with a clear job brief and translating it into a scorecard so every candidate is assessed consistently.
A typical strong process includes an initial screen, a relevant technical assessment and one or two deeper conversations focused on real work scenarios. Long, drawn out interview loops and irrelevant tasks are one of the fastest ways to lose good candidates.
Why Time Kills Hiring
Slow feedback, unclear salary ranges and delayed interview scheduling all work against early stage companies. Top candidates are usually speaking to multiple teams. Founders who move quickly and communicate clearly have a significant advantage.
Speed does not mean lowering standards. It means removing unnecessary friction.
Competing With Big Tech for AI Talent
Early stage SaaS companies cannot compete with big tech on brand or salary alone. What they can offer is ownership, impact and the opportunity to shape systems from the ground up.
Greenfield work, architectural influence and meaningful equity are powerful motivators for senior engineers. The key is positioning these strengths clearly and honestly.
The Value of Ecosystem Hiring
Hiring from within your ecosystem reduces risk and speeds up time to value. Engineers and operators who already understand your industry, customer and problem space ramp up faster and make better decisions early on.
Ecosystem experience does not guarantee success, but it significantly lowers uncertainty, which is critical in small teams.
The Most Overrated and Underrated Roles
According to Jay, prompt engineer is one of the most overrated AI job titles right now. Prompting should be a baseline skill for anyone working with AI, not a standalone role.
One of the most underrated roles in early stage SaaS is solutions engineering. These are technically capable, customer facing individuals who can support sales, implementations and real world delivery without being full time developers.
The First AI Hire Most Founders Should Make
In most cases, the right first hire is a senior individual contributor who can build, ship and guide technical decisions. This person should be comfortable with ambiguity, able to mentor others and focused on delivery rather than perfection.
Specialist roles make sense later, once the product and team have matured.
One Piece of Advice for Founders
Jay’s advice is simple. Get clear before you hire. Define the problem, the stage and the outcome you need. Then hire accordingly.
Most AI hiring mistakes are not caused by talent shortages. They are caused by unclear thinking.
If you want to follow Jay’s work, you can connect with him on LinkedIn or learn more about WunderTalent. And if you are hiring or exploring your next SaaS role, you can find thousands of opportunities on The SaaS Jobs and use our AI powered matching tool, the SaaS Career Co-pilot.
