Why the Best AI Engineers Aren’t Chasing the Highest Salary
The competition for AI talent in FinTech has never been more intense. As artificial intelligence moves from experimentation to production, engineering leaders are under growing pressure to attract people who can not only build AI systems, but deploy them safely, ethically, and at scale.
Yet despite soaring salaries and aggressive hiring campaigns, many FinTech businesses are discovering that money alone is no longer enough.
In this episode of FinTech Focus TV, hosted by Toby Babb, two AI-native FinTech leaders, Cyrus Lyons, Head of Engineering at Tunic Pay, and Neal Lathia, Co-Founder and CTO at Gradient Labs, unpack what actually attracts, engages, and retains top AI engineers today
Their insights challenge many of the assumptions still shaping AI hiring strategies across financial services.
The AI Talent Market Has Changed Faster Than Hiring Models
For years, FinTech hiring followed a familiar pattern. Competitive compensation, clearly defined roles, and structured career progression were the primary levers for attracting engineers.
AI has disrupted that model.
According to Lyons and Lathia, the pace of change in AI tooling and capability has created a new kind of engineer, one less motivated by hierarchy and more driven by impact. Many of the strongest candidates have multiple offers, often from well-funded firms willing to pay a premium. Yet those offers do not always win.
What has changed is not demand, but motivation.
Why Salary Is No Longer the Deciding Factor
Early-stage FinTechs cannot compete with Big Tech or global financial institutions on base pay alone. But as both guests explain, they do not need to.
AI engineers who choose startups often do so precisely because they are not boxed into narrow roles. They want proximity to real problems, fast feedback loops, and the ability to see their work in production.
At companies like Tunic Pay and Gradient Labs, engineers are close to customers, regulators, and real-world outcomes. That proximity creates a sense of ownership that salary alone cannot replicate.
Purpose, Ownership, and Problem Depth
Both businesses operate in domains with tangible societal impact. Tunic Pay focuses on preventing payment scams, a growing financial crime affecting millions of consumers. Gradient Labs builds AI agents that transform customer support in regulated financial environments.
This sense of purpose matters. Engineers are not simply optimising models; they are solving problems with visible consequences. As Lyons notes, that combination of scale and responsibility creates a compelling value proposition for the right candidates.
AI-Native Teams Operate Differently
One of the most striking insights from the episode is how AI-native companies structure their teams.
Rather than rigid hierarchies, both organisations prioritise fluid collaboration. Engineers are not siloed into permanent teams but move between problems as priorities evolve. Meetings are minimised. Calendars are kept deliberately light. Building is the default activity.
This environment appeals to engineers who want to stay close to the technology as it evolves. As Lathia explains, AI is reshaping not just products, but how engineering itself is practiced.
Hiring for Competence, Not Job Titles
AI engineering roles remain poorly standardised. Job titles vary wildly, and CVs often fail to capture how candidates actually work.
Instead of filtering on titles or years of experience, both companies focus on competencies: problem-solving ability, curiosity, and comfort working with uncertainty.
Traditional systems design interviews have been replaced with product-focused discussions. Candidates are encouraged to use AI tools openly, with transparency valued over purity. The goal is not to catch people out, but to understand how they think.
Retention Starts on Day One
Attracting AI talent is only half the challenge. Retaining it requires alignment between expectation and reality.
The guests argue that short tenures in engineering often stem from misaligned hiring. When people are recruited into roles that underuse their capabilities, disengagement is inevitable.
By contrast, environments that prioritise autonomy, experimentation, and trust tend to retain people longer, even in a highly competitive market.
What This Means for FinTech Hiring Leaders
For FinTech technology leaders, the implications are clear:
- Salary matters, but it is not decisive
- Engineers want meaningful ownership
- AI-native environments require new hiring frameworks
- Retention is driven by day-to-day experience, not perks
For organisations working with partners like Harrington Starr, these insights reinforce the importance of hiring strategy over speed.
As AI continues to reshape financial services, the firms that win talent will be those that understand what engineers value, and design their cultures accordingly.
