How HeyGen uses AI w/ Head of Product Engineering Nikunj Yadav
HeyGen is a rocket ship in the AI video space with now over 85,000 customers using their platform to create hyper-realistic video avatars. We spoke to their Head of Product Engineering Nikunj Yadav to unpack how his team uses AI across the dev cycle.
We get into how AI is helping backend engineers write front-end code, helping non-engineering teams self-serve technical answers, and helping everyone ship end-to-end with fewer handoffs.
Here are our takeaways:
From specialists to assistive full-stack builders
For Nikunj, AI has blurred the line between backend and frontend engineers.
“Especially writing front end is much, much easier than it would have been without it.”
His team uses Cursor to generate front-end components in React and Tailwind, including engineers who don’t consider themselves UI specialists. The impact compounds: fewer handoffs, less coordination overhead, and faster end-to-end delivery.
“Backend engineers can now write the front end themselves - they know just enough to review what AI generates.”
Nikunj predicts this shift will eventually change how teams are structured. Every engineer can now take ownership across the stack.
Where AI delivers the most lift: codegen and testing
When asked which part of the dev cycle AI helps most, Nikunj said code generation is still the biggest boost.
But right behind it? Testing.
“We went from zero backend tests to reasonable coverage, and AI got us a lot of the way there.”
Developers at HeyGen now prompt AI to write unit tests - supplying the test cases, mocking instructions, and examples of past tests. It’s a practical workflow: they still design the logic and edge cases, but AI handles the repetitive setup.
The result is better coverage, faster feedback, and far less friction between coding and validation.
The dream: self-healing test suites
Still, Nikunj sees a huge opportunity ahead.
“Maybe there’s a CLI tool we could write that updates old tests automatically. That’s the dream.”
Most teams know the pain of outdated tests breaking every other CI run. A repo-aware AI that could rewrite and adapt tests after each change would be a genuine velocity unlock.
AI in review: good, but not great (yet)
HeyGen’s team also uses Graphite, both for AI-assisted review and for stacked PRs - a practice that lets engineers push multiple small changes in parallel.
The AI features have shown promise, catching minor bugs and improving with time. But Nikunj sees more potential.
“It’s still operating at a very fundamental level, like this thing looks broken. It could do more, like ‘this is a code smell, rethink this design.’”
For now, Graphite acts as a kind of intelligent CI: a first line of review before human eyes. Nikunj thinks the future lies in pattern-level feedback, where AI can spot design issues, not just syntax errors.
Hiring for problem-solvers, interviewing for AI fluency
A lot has changed at HeyGen, but one thing that hasn’t changed? What they look for in engineers.
“We’re always hiring people who are really good at problem solving.. that’s been true forever.”
What has changed is how they interview. Candidates are explicitly allowed to use AI tools like Cursor during their coding exercises.
The goal isn’t to see if they can memorize syntax - it’s to see how they think when working with AI output that’s imperfect.
“Watching how someone reviews and debugs AI-generated code gives you a whole new level of insight.”
It’s an interview design that reflects the new normal: AI will always make mistakes, and great engineers are the ones who can spot and fix them, fast.
How support and engineering collab
Not every increase in productivity comes from code generation. Non-engineering teams (especially customer support and success) use AI to self-serve customer questions, dramatically cutting down how often engineers get pulled into Slack threads or support requests.
“If an engineer isn’t getting bothered a few times a day, they’re just going to do a lot more.”
AI, in this sense, protects focus by reducing the number of contexts engineers have to switch.
In conclusion
At HeyGen, AI has expanded what the engineering team is capable of. It has made backend engineers front-end capable, brought testing coverage up from zero, and freed developers from constant context-switching. Across the conversations we've had in this series, there's a clear pattern. Companies that create a culture around frequent, light-weight experimentation are deriving the most value out of AI.
We had such a great time jamming with Nikunj! You can catch our full conversation on YouTube, alongside episodes with engineering and product leaders from Intercom, Monday.com, and Vercel.