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In this episode, I sat down with Mojtaba Hosseini, VP of Engineering at Zapier, to talk about how their team builds for long-term performance. We explored how they stay close to customers, make learning a habit, and use metrics that actually help the team improve. Mojtaba also shared how they're using AI in practical ways across the company.
What You’ll Learn
Why engineering is applied science in service of the customer
How customer focus shapes culture and drives engineering decisions
What “high performance” really means, and why it’s about the slope of learning
How Zapier built a team-driven approach to metrics over four years
What to measure—and what not to—when evaluating developer productivity
How engineering can support other teams in adopting AI
Why “always be learning” is a core engineering principle
How to build habits of sharing and experimentation across teams
“When I think of high performance, it's not a bar a team needs to hit. It's the slope at which it's learning. Because that bar is always moving — what was magical five years ago is now table stakes. The teams that win are the ones learning fast enough to keep up with what customers expect next.” — Mojtaba Hosseini, VP of Engineering at Zapier
Mojtaba Hosseini is VP of Engineering at Zapier, where he’s spent the last several years building high-performing teams grounded in customer focus, data, and continuous learning. With a systems-thinking approach to leadership, Mojtaba has led Zapier’s engineering team through a four-year journey of defining meaningful metrics, shifting measurement from a top-down requirement to a team-driven habit.
He’s been leading Zapier’s efforts to bring AI into day-to-day work—helping engineers work more efficiently and supporting other teams across the company. He combines deep technical experience with hands-on leadership to build engineering teams that scale with focus, clarity, and impact.
Resources:
Content Chapters:
00:00 Introduction to Engineering Leadership at Zapier
03:50 Customer-Centric Engineering: The Feedback Loop
10:05 Defining High Performance in Engineering
15:29 The Journey of Implementing Engineering Metrics
26:37 Evaluating Metric Effectiveness
30:57 Leveraging AI for Developer Efficiency
34:19 Cross-Departmental Collaboration and AI Integration
38:51 Wrapping Up
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